Tag Archives: column

Build a Binary Clock with engineerish

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/engineerish-binary-clock/

Standard clocks with easily recognisable numbers are so last season. Who wants to save valuable seconds simply telling the time, when a series of LEDs and numerical notation can turn every time query into an adventure in mathematics?

Build a Binary Clock with Raspberry Pi – And how to tell the time

In this video I’ll be showing how I built a binary clock using a Raspberry Pi, NeoPixels and a few lines of Python. I also take a stab at explaining how the binary number system works so that we can decipher what said clock is trying to tell us.

How to read binary

I’ll be honest: I have to think pretty hard to read binary. It stretches my brain quite vigorously. But I am a fan of flashy lights and pretty builds, so YouTube and Instagram rising star Mattias Jähnke, aka engineerish, had my full attention from the off.

“If you have a problem with your friends being able to tell the time way too easily while in your house, this is your answer.”

Mattias offers a beginners’ guide in to binary in his video and then explains how his clock displays values in binary, before moving on to the actual clock build process. So make some tea, pull up a chair, and jump right in.

Binary clock

To build the clock, Mattias used a Raspberry Pi and NeoPixel strips, fitted snugly within a simple 3D-printed case. With a few lines of Python, he coded his clock to display the current time using the binary system, with columns for seconds, minutes, and hours.

The real kicker with a binary clock is that by the time you’ve deciphered what time it is – you’re probably already late.

418 Likes, 14 Comments – Mattias (@engineerish) on Instagram: “The real kicker with a binary clock is that by the time you’ve deciphered what time it is – you’re…”

The Python code isn’t currently available on Mattias’s GitHub account, but if you’re keen to see how he did it, and you ask politely, and he’s not too busy, you never know.

Make your own

In the meantime, while we batter our eyelashes in the general direction of Stockholm and hope for a response, I challenge any one of you to code a binary display project for the Raspberry Pi. It doesn’t have to be a clock. And it doesn’t have to use NeoPixels. Maybe it could use an LED matrix such as the SenseHat, or a series of independently controlled LEDs on a breadboard. Maybe there’s something to be done with servo motors that flip discs with different-coloured sides to display a binary number.

Whatever you decide to build, the standard reward applies: ten imaginary house points (of absolutely no practical use, but immense emotional value) and a great sense of achievement to all who give it a go.

The post Build a Binary Clock with engineerish appeared first on Raspberry Pi.

Eevee gained 2791 experience points

Post Syndicated from Eevee original https://eev.ee/blog/2018/01/15/eevee-gained-2791-experience-points/

Eevee grew to level 31!

A year strongly defined by mixed success! Also, a lot of video games.

I ran three game jams, resulting in a total of 157 games existing that may not have otherwise, which is totally mindblowing?!

For GAMES MADE QUICK???, glip and I made NEON PHASE, a short little exploratory platformer. Honestly, I should give myself more credit for this and the rest of the LÖVE games I’ve based on the same codebase — I wove a physics engine (and everything else!) from scratch and it has held up remarkably well for a variety of different uses.

I successfully finished an HD version of Isaac’s Descent using my LÖVE engine, though it doesn’t have anything new over the original and I’ve only released it as a tech demo on Patreon.

For Strawberry Jam (NSFW!) we made fox flux (slightly NSFW!), which felt like a huge milestone: the first game where I made all the art! I mean, not counting Isaac’s Descent, which was for a very limited platform. It’s a pretty arbitrary milestone, yes, but it feels significant. I’ve been working on expanding the game into a longer and slightly less buggy experience, but the art is taking the longest by far. I must’ve spent weeks on player sprites alone.

We then set about working on Bolthaven, a sequel of sorts to NEON PHASE, and got decently far, and then abandond it. Oops.

We then started a cute little PICO-8 game, and forgot about it. Oops.

I was recruited to help with Chaos Composer, a more ambitious game glip started with someone else in Unity. I had to get used to Unity, and we squabbled a bit, but the game is finally about at the point where it’s “playable” and “maps” can be designed? It’s slightly on hold at the moment while we all finish up some other stuff, though.

We made a birthday game for two of our friends whose birthdays were very close together! Only they got to see it.

For Ludum Dare 38, we made Lunar Depot 38, a little “wave shooter” or whatever you call those? The AI is pretty rough, seeing as this was the first time I’d really made enemies and I had 72 hours to figure out how to do it, but I still think it’s pretty fun to play and I love the circular world.

I made Roguelike Simulator as an experiment with making something small and quick with a simple tool, and I had a lot of fun! I definitely want to do more stuff like this in the future.

And now we’re working on a game about Star Anise, my cat’s self-insert, which is looking to have more polish and depth than anything we’ve done so far! We’ve definitely come a long way in a year.

Somewhere along the line, I put out a call for a “potluck” project, where everyone would give me sprites of a given size without knowing what anyone else had contributed, and I would then make a game using only those sprites. Unfortunately, that stalled a few times: I tried using the Phaser JS library, but we didn’t get along; I tried LÖVE, but didn’t know where to go with the game; and then I decided to use this as an experiment with procedural generation, and didn’t get around to it. I still feel bad that everyone did work for me and I didn’t follow through, but I don’t know whether this will ever become a game.

veekun, alas, consumed months of my life. I finally got Sun and Moon loaded, but it took weeks of work since I was basically reinventing all the tooling we’d ever had from scratch, without even having most of that tooling available as a reference. It was worth it in the end, at least: Ultra Sun and Ultra Moon only took a few days to get loaded. But veekun itself is still missing some obvious Sun/Moon features, and the whole site needs an overhaul, and I just don’t know if I want to dedicate that much time to it when I have so much other stuff going on that’s much more interesting to me right now.

I finally turned my blog into more of a website, giving it a neat front page that lists a bunch of stuff I’ve done. I made a release category at last, though I’m still not quite in the habit of using it.

I wrote some blog posts, of course! I think the most interesting were JavaScript got better while I wasn’t looking and Object models. I was also asked to write a couple pieces for money for a column that then promptly shut down.

On a whim, I made a set of Eevee mugshots for Doom, which I think is a decent indication of my (pixel) art progress over the year?

I started idchoppers, a Doom parsing and manipulation library written in Rust, though it didn’t get very far and I’ve spent most of the time fighting with Rust because it won’t let me implement all my extremely bad ideas. It can do a couple things, at least, like flip maps very quickly and render maps to SVG.

I did toy around with music a little, but not a lot.

I wrote two short twines for Flora. They’re okay. I’m working on another; I think it’ll be better.

I didn’t do a lot of art overall, at least compared to the two previous years; most of my art effort over the year has gone into fox flux, which requires me to learn a whole lot of things. I did dip my toes into 3D modelling, most notably producing my current Twitter banner as well as this cool Star Anise animation. I wouldn’t mind doing more of that; maybe I’ll even try to make a low-poly pixel-textured 3D game sometime.

I restarted my book with a much better concept, though so far I’ve only written about half a chapter. Argh. I see that the vast majority of the work was done within the span of a single week, which is bad since that means I only worked on it for a week, but good since that means I can actually do a pretty good amount of work in only a week. I also did a lot of squabbling with tooling, which is hopefully mostly out of the way now.

My computer broke? That was an exciting week.


A lot of stuff, but the year as a whole still feels hit or miss. All the time I spent on veekun feels like a black void in the middle of the year, which seems like a good sign that I maybe don’t want to pour even more weeks into it in the near future.

Mostly, I want to do: more games, more art, more writing, more music.

I want to try out some tiny game making tools and make some tiny games with them — partly to get exposure to different things, partly to get more little ideas out into the world regularly, and partly to get more practice at letting myself have ideas. I have a couple tools in mind and I guess I’ll aim at a microgame every two months or so? I’d also like to finish the expanded fox flux by the end of the year, of course, though at the moment I can’t even gauge how long it might take.

I seriously lapsed on drawing last year, largely because fox flux pixel art took me so much time. So I want to draw more, and I want to get much faster at pixel art. It would probably help if I had a more concrete goal for drawing, so I might try to draw some short comics and write a little visual novel or something, which would also force me to aim for consistency.

I want to work on my book more, of course, but I also want to try my hand at a bit more fiction. I’ve had a blast writing dialogue for our games! I just shy away from longer-form writing for some reason — which seems ridiculous when a large part of my audience found me through my blog. I do think I’ve had some sort of breakthrough in the last month or two; I suddenly feel a good bit more confident about writing in general and figuring out what I want to say? One recent post I know I wrote in a single afternoon, which virtually never happens because I keep rewriting and rearranging stuff. Again, a visual novel would be a good excuse to practice writing fiction without getting too bogged down in details.

And, ah, music. I shy heavily away from music, since I have no idea what I’m doing, and also I seem to spend a lot of time fighting with tools. (Surprise.) I tried out SunVox for the first time just a few days ago and have been enjoying it quite a bit for making sound effects, so I might try it for music as well. And once again, visual novel background music is a pretty low-pressure thing to compose for. Hell, visual novels are small games, too, so that checks all the boxes. I guess I’ll go make a visual novel.

Here’s to twenty gayteen!

AWS Glue Now Supports Scala Scripts

Post Syndicated from Mehul Shah original https://aws.amazon.com/blogs/big-data/aws-glue-now-supports-scala-scripts/

We are excited to announce AWS Glue support for running ETL (extract, transform, and load) scripts in Scala. Scala lovers can rejoice because they now have one more powerful tool in their arsenal. Scala is the native language for Apache Spark, the underlying engine that AWS Glue offers for performing data transformations.

Beyond its elegant language features, writing Scala scripts for AWS Glue has two main advantages over writing scripts in Python. First, Scala is faster for custom transformations that do a lot of heavy lifting because there is no need to shovel data between Python and Apache Spark’s Scala runtime (that is, the Java virtual machine, or JVM). You can build your own transformations or invoke functions in third-party libraries. Second, it’s simpler to call functions in external Java class libraries from Scala because Scala is designed to be Java-compatible. It compiles to the same bytecode, and its data structures don’t need to be converted.

To illustrate these benefits, we walk through an example that analyzes a recent sample of the GitHub public timeline available from the GitHub archive. This site is an archive of public requests to the GitHub service, recording more than 35 event types ranging from commits and forks to issues and comments.

This post shows how to build an example Scala script that identifies highly negative issues in the timeline. It pulls out issue events in the timeline sample, analyzes their titles using the sentiment prediction functions from the Stanford CoreNLP libraries, and surfaces the most negative issues.

Getting started

Before we start writing scripts, we use AWS Glue crawlers to get a sense of the data—its structure and characteristics. We also set up a development endpoint and attach an Apache Zeppelin notebook, so we can interactively explore the data and author the script.

Crawl the data

The dataset used in this example was downloaded from the GitHub archive website into our sample dataset bucket in Amazon S3, and copied to the following locations:

s3://aws-glue-datasets-<region>/examples/scala-blog/githubarchive/data/

Choose the best folder by replacing <region> with the region that you’re working in, for example, us-east-1. Crawl this folder, and put the results into a database named githubarchive in the AWS Glue Data Catalog, as described in the AWS Glue Developer Guide. This folder contains 12 hours of the timeline from January 22, 2017, and is organized hierarchically (that is, partitioned) by year, month, and day.

When finished, use the AWS Glue console to navigate to the table named data in the githubarchive database. Notice that this data has eight top-level columns, which are common to each event type, and three partition columns that correspond to year, month, and day.

Choose the payload column, and you will notice that it has a complex schema—one that reflects the union of the payloads of event types that appear in the crawled data. Also note that the schema that crawlers generate is a subset of the true schema because they sample only a subset of the data.

Set up the library, development endpoint, and notebook

Next, you need to download and set up the libraries that estimate the sentiment in a snippet of text. The Stanford CoreNLP libraries contain a number of human language processing tools, including sentiment prediction.

Download the Stanford CoreNLP libraries. Unzip the .zip file, and you’ll see a directory full of jar files. For this example, the following jars are required:

  • stanford-corenlp-3.8.0.jar
  • stanford-corenlp-3.8.0-models.jar
  • ejml-0.23.jar

Upload these files to an Amazon S3 path that is accessible to AWS Glue so that it can load these libraries when needed. For this example, they are in s3://glue-sample-other/corenlp/.

Development endpoints are static Spark-based environments that can serve as the backend for data exploration. You can attach notebooks to these endpoints to interactively send commands and explore and analyze your data. These endpoints have the same configuration as that of AWS Glue’s job execution system. So, commands and scripts that work there also work the same when registered and run as jobs in AWS Glue.

To set up an endpoint and a Zeppelin notebook to work with that endpoint, follow the instructions in the AWS Glue Developer Guide. When you are creating an endpoint, be sure to specify the locations of the previously mentioned jars in the Dependent jars path as a comma-separated list. Otherwise, the libraries will not be loaded.

After you set up the notebook server, go to the Zeppelin notebook by choosing Dev Endpoints in the left navigation pane on the AWS Glue console. Choose the endpoint that you created. Next, choose the Notebook Server URL, which takes you to the Zeppelin server. Log in using the notebook user name and password that you specified when creating the notebook. Finally, create a new note to try out this example.

Each notebook is a collection of paragraphs, and each paragraph contains a sequence of commands and the output for that command. Moreover, each notebook includes a number of interpreters. If you set up the Zeppelin server using the console, the (Python-based) pyspark and (Scala-based) spark interpreters are already connected to your new development endpoint, with pyspark as the default. Therefore, throughout this example, you need to prepend %spark at the top of your paragraphs. In this example, we omit these for brevity.

Working with the data

In this section, we use AWS Glue extensions to Spark to work with the dataset. We look at the actual schema of the data and filter out the interesting event types for our analysis.

Start with some boilerplate code to import libraries that you need:

%spark

import com.amazonaws.services.glue.DynamicRecord
import com.amazonaws.services.glue.GlueContext
import com.amazonaws.services.glue.util.GlueArgParser
import com.amazonaws.services.glue.util.Job
import com.amazonaws.services.glue.util.JsonOptions
import com.amazonaws.services.glue.types._
import org.apache.spark.SparkContext

Then, create the Spark and AWS Glue contexts needed for working with the data:

@transient val spark: SparkContext = SparkContext.getOrCreate()
val glueContext: GlueContext = new GlueContext(spark)

You need the transient decorator on the SparkContext when working in Zeppelin; otherwise, you will run into a serialization error when executing commands.

Dynamic frames

This section shows how to create a dynamic frame that contains the GitHub records in the table that you crawled earlier. A dynamic frame is the basic data structure in AWS Glue scripts. It is like an Apache Spark data frame, except that it is designed and optimized for data cleaning and transformation workloads. A dynamic frame is well-suited for representing semi-structured datasets like the GitHub timeline.

A dynamic frame is a collection of dynamic records. In Spark lingo, it is an RDD (resilient distributed dataset) of DynamicRecords. A dynamic record is a self-describing record. Each record encodes its columns and types, so every record can have a schema that is unique from all others in the dynamic frame. This is convenient and often more efficient for datasets like the GitHub timeline, where payloads can vary drastically from one event type to another.

The following creates a dynamic frame, github_events, from your table:

val github_events = glueContext
                    .getCatalogSource(database = "githubarchive", tableName = "data")
                    .getDynamicFrame()

The getCatalogSource() method returns a DataSource, which represents a particular table in the Data Catalog. The getDynamicFrame() method returns a dynamic frame from the source.

Recall that the crawler created a schema from only a sample of the data. You can scan the entire dataset, count the rows, and print the complete schema as follows:

github_events.count
github_events.printSchema()

The result looks like the following:

The data has 414,826 records. As before, notice that there are eight top-level columns, and three partition columns. If you scroll down, you’ll also notice that the payload is the most complex column.

Run functions and filter records

This section describes how you can create your own functions and invoke them seamlessly to filter records. Unlike filtering with Python lambdas, Scala scripts do not need to convert records from one language representation to another, thereby reducing overhead and running much faster.

Let’s create a function that picks only the IssuesEvents from the GitHub timeline. These events are generated whenever someone posts an issue for a particular repository. Each GitHub event record has a field, “type”, that indicates the kind of event it is. The issueFilter() function returns true for records that are IssuesEvents.

def issueFilter(rec: DynamicRecord): Boolean = { 
    rec.getField("type").exists(_ == "IssuesEvent") 
}

Note that the getField() method returns an Option[Any] type, so you first need to check that it exists before checking the type.

You pass this function to the filter transformation, which applies the function on each record and returns a dynamic frame of those records that pass.

val issue_events =  github_events.filter(issueFilter)

Now, let’s look at the size and schema of issue_events.

issue_events.count
issue_events.printSchema()

It’s much smaller (14,063 records), and the payload schema is less complex, reflecting only the schema for issues. Keep a few essential columns for your analysis, and drop the rest using the ApplyMapping() transform:

val issue_titles = issue_events.applyMapping(Seq(("id", "string", "id", "string"),
                                                 ("actor.login", "string", "actor", "string"), 
                                                 ("repo.name", "string", "repo", "string"),
                                                 ("payload.action", "string", "action", "string"),
                                                 ("payload.issue.title", "string", "title", "string")))
issue_titles.show()

The ApplyMapping() transform is quite handy for renaming columns, casting types, and restructuring records. The preceding code snippet tells the transform to select the fields (or columns) that are enumerated in the left half of the tuples and map them to the fields and types in the right half.

Estimating sentiment using Stanford CoreNLP

To focus on the most pressing issues, you might want to isolate the records with the most negative sentiments. The Stanford CoreNLP libraries are Java-based and offer sentiment-prediction functions. Accessing these functions through Python is possible, but quite cumbersome. It requires creating Python surrogate classes and objects for those found on the Java side. Instead, with Scala support, you can use those classes and objects directly and invoke their methods. Let’s see how.

First, import the libraries needed for the analysis:

import java.util.Properties
import edu.stanford.nlp.ling.CoreAnnotations
import edu.stanford.nlp.neural.rnn.RNNCoreAnnotations
import edu.stanford.nlp.pipeline.{Annotation, StanfordCoreNLP}
import edu.stanford.nlp.sentiment.SentimentCoreAnnotations
import scala.collection.convert.wrapAll._

The Stanford CoreNLP libraries have a main driver that orchestrates all of their analysis. The driver setup is heavyweight, setting up threads and data structures that are shared across analyses. Apache Spark runs on a cluster with a main driver process and a collection of backend executor processes that do most of the heavy sifting of the data.

The Stanford CoreNLP shared objects are not serializable, so they cannot be distributed easily across a cluster. Instead, you need to initialize them once for every backend executor process that might need them. Here is how to accomplish that:

val props = new Properties()
props.setProperty("annotators", "tokenize, ssplit, parse, sentiment")
props.setProperty("parse.maxlen", "70")

object myNLP {
    lazy val coreNLP = new StanfordCoreNLP(props)
}

The properties tell the libraries which annotators to execute and how many words to process. The preceding code creates an object, myNLP, with a field coreNLP that is lazily evaluated. This field is initialized only when it is needed, and only once. So, when the backend executors start processing the records, each executor initializes the driver for the Stanford CoreNLP libraries only one time.

Next is a function that estimates the sentiment of a text string. It first calls Stanford CoreNLP to annotate the text. Then, it pulls out the sentences and takes the average sentiment across all the sentences. The sentiment is a double, from 0.0 as the most negative to 4.0 as the most positive.

def estimatedSentiment(text: String): Double = {
    if ((text == null) || (!text.nonEmpty)) { return Double.NaN }
    val annotations = myNLP.coreNLP.process(text)
    val sentences = annotations.get(classOf[CoreAnnotations.SentencesAnnotation])
    sentences.foldLeft(0.0)( (csum, x) => { 
        csum + RNNCoreAnnotations.getPredictedClass(x.get(classOf[SentimentCoreAnnotations.SentimentAnnotatedTree])) 
    }) / sentences.length
}

Now, let’s estimate the sentiment of the issue titles and add that computed field as part of the records. You can accomplish this with the map() method on dynamic frames:

val issue_sentiments = issue_titles.map((rec: DynamicRecord) => { 
    val mbody = rec.getField("title")
    mbody match {
        case Some(mval: String) => { 
            rec.addField("sentiment", ScalarNode(estimatedSentiment(mval)))
            rec }
        case _ => rec
    }
})

The map() method applies the user-provided function on every record. The function takes a DynamicRecord as an argument and returns a DynamicRecord. The code above computes the sentiment, adds it in a top-level field, sentiment, to the record, and returns the record.

Count the records with sentiment and show the schema. This takes a few minutes because Spark must initialize the library and run the sentiment analysis, which can be involved.

issue_sentiments.count
issue_sentiments.printSchema()

Notice that all records were processed (14,063), and the sentiment value was added to the schema.

Finally, let’s pick out the titles that have the lowest sentiment (less than 1.5). Count them and print out a sample to see what some of the titles look like.

val pressing_issues = issue_sentiments.filter(_.getField("sentiment").exists(_.asInstanceOf[Double] < 1.5))
pressing_issues.count
pressing_issues.show(10)

Next, write them all to a file so that you can handle them later. (You’ll need to replace the output path with your own.)

glueContext.getSinkWithFormat(connectionType = "s3", 
                              options = JsonOptions("""{"path": "s3://<bucket>/out/path/"}"""), 
                              format = "json")
            .writeDynamicFrame(pressing_issues)

Take a look in the output path, and you can see the output files.

Putting it all together

Now, let’s create a job from the preceding interactive session. The following script combines all the commands from earlier. It processes the GitHub archive files and writes out the highly negative issues:

import com.amazonaws.services.glue.DynamicRecord
import com.amazonaws.services.glue.GlueContext
import com.amazonaws.services.glue.util.GlueArgParser
import com.amazonaws.services.glue.util.Job
import com.amazonaws.services.glue.util.JsonOptions
import com.amazonaws.services.glue.types._
import org.apache.spark.SparkContext
import java.util.Properties
import edu.stanford.nlp.ling.CoreAnnotations
import edu.stanford.nlp.neural.rnn.RNNCoreAnnotations
import edu.stanford.nlp.pipeline.{Annotation, StanfordCoreNLP}
import edu.stanford.nlp.sentiment.SentimentCoreAnnotations
import scala.collection.convert.wrapAll._

object GlueApp {

    object myNLP {
        val props = new Properties()
        props.setProperty("annotators", "tokenize, ssplit, parse, sentiment")
        props.setProperty("parse.maxlen", "70")

        lazy val coreNLP = new StanfordCoreNLP(props)
    }

    def estimatedSentiment(text: String): Double = {
        if ((text == null) || (!text.nonEmpty)) { return Double.NaN }
        val annotations = myNLP.coreNLP.process(text)
        val sentences = annotations.get(classOf[CoreAnnotations.SentencesAnnotation])
        sentences.foldLeft(0.0)( (csum, x) => { 
            csum + RNNCoreAnnotations.getPredictedClass(x.get(classOf[SentimentCoreAnnotations.SentimentAnnotatedTree])) 
        }) / sentences.length
    }

    def main(sysArgs: Array[String]) {
        val spark: SparkContext = SparkContext.getOrCreate()
        val glueContext: GlueContext = new GlueContext(spark)

        val dbname = "githubarchive"
        val tblname = "data"
        val outpath = "s3://<bucket>/out/path/"

        val github_events = glueContext
                            .getCatalogSource(database = dbname, tableName = tblname)
                            .getDynamicFrame()

        val issue_events =  github_events.filter((rec: DynamicRecord) => {
            rec.getField("type").exists(_ == "IssuesEvent")
        })

        val issue_titles = issue_events.applyMapping(Seq(("id", "string", "id", "string"),
                                                         ("actor.login", "string", "actor", "string"), 
                                                         ("repo.name", "string", "repo", "string"),
                                                         ("payload.action", "string", "action", "string"),
                                                         ("payload.issue.title", "string", "title", "string")))

        val issue_sentiments = issue_titles.map((rec: DynamicRecord) => { 
            val mbody = rec.getField("title")
            mbody match {
                case Some(mval: String) => { 
                    rec.addField("sentiment", ScalarNode(estimatedSentiment(mval)))
                    rec }
                case _ => rec
            }
        })

        val pressing_issues = issue_sentiments.filter(_.getField("sentiment").exists(_.asInstanceOf[Double] < 1.5))

        glueContext.getSinkWithFormat(connectionType = "s3", 
                              options = JsonOptions(s"""{"path": "$outpath"}"""), 
                              format = "json")
                    .writeDynamicFrame(pressing_issues)
    }
}

Notice that the script is enclosed in a top-level object called GlueApp, which serves as the script’s entry point for the job. (You’ll need to replace the output path with your own.) Upload the script to an Amazon S3 location so that AWS Glue can load it when needed.

To create the job, open the AWS Glue console. Choose Jobs in the left navigation pane, and then choose Add job. Create a name for the job, and specify a role with permissions to access the data. Choose An existing script that you provide, and choose Scala as the language.

For the Scala class name, type GlueApp to indicate the script’s entry point. Specify the Amazon S3 location of the script.

Choose Script libraries and job parameters. In the Dependent jars path field, enter the Amazon S3 locations of the Stanford CoreNLP libraries from earlier as a comma-separated list (without spaces). Then choose Next.

No connections are needed for this job, so choose Next again. Review the job properties, and choose Finish. Finally, choose Run job to execute the job.

You can simply edit the script’s input table and output path to run this job on whatever GitHub timeline datasets that you might have.

Conclusion

In this post, we showed how to write AWS Glue ETL scripts in Scala via notebooks and how to run them as jobs. Scala has the advantage that it is the native language for the Spark runtime. With Scala, it is easier to call Scala or Java functions and third-party libraries for analyses. Moreover, data processing is faster in Scala because there’s no need to convert records from one language runtime to another.

You can find more example of Scala scripts in our GitHub examples repository: https://github.com/awslabs/aws-glue-samples. We encourage you to experiment with Scala scripts and let us know about any interesting ETL flows that you want to share.

Happy Glue-ing!

 


Additional Reading

If you found this post useful, be sure to check out Simplify Querying Nested JSON with the AWS Glue Relationalize Transform and Genomic Analysis with Hail on Amazon EMR and Amazon Athena.

 


About the Authors

Mehul Shah is a senior software manager for AWS Glue. His passion is leveraging the cloud to build smarter, more efficient, and easier to use data systems. He has three girls, and, therefore, he has no spare time.

 

 

 

Ben Sowell is a software development engineer at AWS Glue.

 

 

 

 
Vinay Vivili is a software development engineer for AWS Glue.

 

 

 

Combine Transactional and Analytical Data Using Amazon Aurora and Amazon Redshift

Post Syndicated from Re Alvarez-Parmar original https://aws.amazon.com/blogs/big-data/combine-transactional-and-analytical-data-using-amazon-aurora-and-amazon-redshift/

A few months ago, we published a blog post about capturing data changes in an Amazon Aurora database and sending it to Amazon Athena and Amazon QuickSight for fast analysis and visualization. In this post, I want to demonstrate how easy it can be to take the data in Aurora and combine it with data in Amazon Redshift using Amazon Redshift Spectrum.

With Amazon Redshift, you can build petabyte-scale data warehouses that unify data from a variety of internal and external sources. Because Amazon Redshift is optimized for complex queries (often involving multiple joins) across large tables, it can handle large volumes of retail, inventory, and financial data without breaking a sweat.

In this post, we describe how to combine data in Aurora in Amazon Redshift. Here’s an overview of the solution:

  • Use AWS Lambda functions with Amazon Aurora to capture data changes in a table.
  • Save data in an Amazon S3
  • Query data using Amazon Redshift Spectrum.

We use the following services:

Serverless architecture for capturing and analyzing Aurora data changes

Consider a scenario in which an e-commerce web application uses Amazon Aurora for a transactional database layer. The company has a sales table that captures every single sale, along with a few corresponding data items. This information is stored as immutable data in a table. Business users want to monitor the sales data and then analyze and visualize it.

In this example, you take the changes in data in an Aurora database table and save it in Amazon S3. After the data is captured in Amazon S3, you combine it with data in your existing Amazon Redshift cluster for analysis.

By the end of this post, you will understand how to capture data events in an Aurora table and push them out to other AWS services using AWS Lambda.

The following diagram shows the flow of data as it occurs in this tutorial:

The starting point in this architecture is a database insert operation in Amazon Aurora. When the insert statement is executed, a custom trigger calls a Lambda function and forwards the inserted data. Lambda writes the data that it received from Amazon Aurora to a Kinesis data delivery stream. Kinesis Data Firehose writes the data to an Amazon S3 bucket. Once the data is in an Amazon S3 bucket, it is queried in place using Amazon Redshift Spectrum.

Creating an Aurora database

First, create a database by following these steps in the Amazon RDS console:

  1. Sign in to the AWS Management Console, and open the Amazon RDS console.
  2. Choose Launch a DB instance, and choose Next.
  3. For Engine, choose Amazon Aurora.
  4. Choose a DB instance class. This example uses a small, since this is not a production database.
  5. In Multi-AZ deployment, choose No.
  6. Configure DB instance identifier, Master username, and Master password.
  7. Launch the DB instance.

After you create the database, use MySQL Workbench to connect to the database using the CNAME from the console. For information about connecting to an Aurora database, see Connecting to an Amazon Aurora DB Cluster.

The following screenshot shows the MySQL Workbench configuration:

Next, create a table in the database by running the following SQL statement:

Create Table
CREATE TABLE Sales (
InvoiceID int NOT NULL AUTO_INCREMENT,
ItemID int NOT NULL,
Category varchar(255),
Price double(10,2), 
Quantity int not NULL,
OrderDate timestamp,
DestinationState varchar(2),
ShippingType varchar(255),
Referral varchar(255),
PRIMARY KEY (InvoiceID)
)

You can now populate the table with some sample data. To generate sample data in your table, copy and run the following script. Ensure that the highlighted (bold) variables are replaced with appropriate values.

#!/usr/bin/python
import MySQLdb
import random
import datetime

db = MySQLdb.connect(host="AURORA_CNAME",
                     user="DBUSER",
                     passwd="DBPASSWORD",
                     db="DB")

states = ("AL","AK","AZ","AR","CA","CO","CT","DE","FL","GA","HI","ID","IL","IN",
"IA","KS","KY","LA","ME","MD","MA","MI","MN","MS","MO","MT","NE","NV","NH","NJ",
"NM","NY","NC","ND","OH","OK","OR","PA","RI","SC","SD","TN","TX","UT","VT","VA",
"WA","WV","WI","WY")

shipping_types = ("Free", "3-Day", "2-Day")

product_categories = ("Garden", "Kitchen", "Office", "Household")
referrals = ("Other", "Friend/Colleague", "Repeat Customer", "Online Ad")

for i in range(0,10):
    item_id = random.randint(1,100)
    state = states[random.randint(0,len(states)-1)]
    shipping_type = shipping_types[random.randint(0,len(shipping_types)-1)]
    product_category = product_categories[random.randint(0,len(product_categories)-1)]
    quantity = random.randint(1,4)
    referral = referrals[random.randint(0,len(referrals)-1)]
    price = random.randint(1,100)
    order_date = datetime.date(2016,random.randint(1,12),random.randint(1,30)).isoformat()

    data_order = (item_id, product_category, price, quantity, order_date, state,
    shipping_type, referral)

    add_order = ("INSERT INTO Sales "
                   "(ItemID, Category, Price, Quantity, OrderDate, DestinationState, \
                   ShippingType, Referral) "
                   "VALUES (%s, %s, %s, %s, %s, %s, %s, %s)")

    cursor = db.cursor()
    cursor.execute(add_order, data_order)

    db.commit()

cursor.close()
db.close() 

The following screenshot shows how the table appears with the sample data:

Sending data from Amazon Aurora to Amazon S3

There are two methods available to send data from Amazon Aurora to Amazon S3:

  • Using a Lambda function
  • Using SELECT INTO OUTFILE S3

To demonstrate the ease of setting up integration between multiple AWS services, we use a Lambda function to send data to Amazon S3 using Amazon Kinesis Data Firehose.

Alternatively, you can use a SELECT INTO OUTFILE S3 statement to query data from an Amazon Aurora DB cluster and save it directly in text files that are stored in an Amazon S3 bucket. However, with this method, there is a delay between the time that the database transaction occurs and the time that the data is exported to Amazon S3 because the default file size threshold is 6 GB.

Creating a Kinesis data delivery stream

The next step is to create a Kinesis data delivery stream, since it’s a dependency of the Lambda function.

To create a delivery stream:

  1. Open the Kinesis Data Firehose console
  2. Choose Create delivery stream.
  3. For Delivery stream name, type AuroraChangesToS3.
  4. For Source, choose Direct PUT.
  5. For Record transformation, choose Disabled.
  6. For Destination, choose Amazon S3.
  7. In the S3 bucket drop-down list, choose an existing bucket, or create a new one.
  8. Enter a prefix if needed, and choose Next.
  9. For Data compression, choose GZIP.
  10. In IAM role, choose either an existing role that has access to write to Amazon S3, or choose to generate one automatically. Choose Next.
  11. Review all the details on the screen, and choose Create delivery stream when you’re finished.

 

Creating a Lambda function

Now you can create a Lambda function that is called every time there is a change that needs to be tracked in the database table. This Lambda function passes the data to the Kinesis data delivery stream that you created earlier.

To create the Lambda function:

  1. Open the AWS Lambda console.
  2. Ensure that you are in the AWS Region where your Amazon Aurora database is located.
  3. If you have no Lambda functions yet, choose Get started now. Otherwise, choose Create function.
  4. Choose Author from scratch.
  5. Give your function a name and select Python 3.6 for Runtime
  6. Choose and existing or create a new Role, the role would need to have access to call firehose:PutRecord
  7. Choose Next on the trigger selection screen.
  8. Paste the following code in the code window. Change the stream_name variable to the Kinesis data delivery stream that you created in the previous step.
  9. Choose File -> Save in the code editor and then choose Save.
import boto3
import json

firehose = boto3.client('firehose')
stream_name = ‘AuroraChangesToS3’


def Kinesis_publish_message(event, context):
    
    firehose_data = (("%s,%s,%s,%s,%s,%s,%s,%s\n") %(event['ItemID'], 
    event['Category'], event['Price'], event['Quantity'],
    event['OrderDate'], event['DestinationState'], event['ShippingType'], 
    event['Referral']))
    
    firehose_data = {'Data': str(firehose_data)}
    print(firehose_data)
    
    firehose.put_record(DeliveryStreamName=stream_name,
    Record=firehose_data)

Note the Amazon Resource Name (ARN) of this Lambda function.

Giving Aurora permissions to invoke a Lambda function

To give Amazon Aurora permissions to invoke a Lambda function, you must attach an IAM role with appropriate permissions to the cluster. For more information, see Invoking a Lambda Function from an Amazon Aurora DB Cluster.

Once you are finished, the Amazon Aurora database has access to invoke a Lambda function.

Creating a stored procedure and a trigger in Amazon Aurora

Now, go back to MySQL Workbench, and run the following command to create a new stored procedure. When this stored procedure is called, it invokes the Lambda function you created. Change the ARN in the following code to your Lambda function’s ARN.

DROP PROCEDURE IF EXISTS CDC_TO_FIREHOSE;
DELIMITER ;;
CREATE PROCEDURE CDC_TO_FIREHOSE (IN ItemID VARCHAR(255), 
									IN Category varchar(255), 
									IN Price double(10,2),
                                    IN Quantity int(11),
                                    IN OrderDate timestamp,
                                    IN DestinationState varchar(2),
                                    IN ShippingType varchar(255),
                                    IN Referral  varchar(255)) LANGUAGE SQL 
BEGIN
  CALL mysql.lambda_async('arn:aws:lambda:us-east-1:XXXXXXXXXXXXX:function:CDCFromAuroraToKinesis', 
     CONCAT('{ "ItemID" : "', ItemID, 
            '", "Category" : "', Category,
            '", "Price" : "', Price,
            '", "Quantity" : "', Quantity, 
            '", "OrderDate" : "', OrderDate, 
            '", "DestinationState" : "', DestinationState, 
            '", "ShippingType" : "', ShippingType, 
            '", "Referral" : "', Referral, '"}')
     );
END
;;
DELIMITER ;

Create a trigger TR_Sales_CDC on the Sales table. When a new record is inserted, this trigger calls the CDC_TO_FIREHOSE stored procedure.

DROP TRIGGER IF EXISTS TR_Sales_CDC;
 
DELIMITER ;;
CREATE TRIGGER TR_Sales_CDC
  AFTER INSERT ON Sales
  FOR EACH ROW
BEGIN
  SELECT  NEW.ItemID , NEW.Category, New.Price, New.Quantity, New.OrderDate
  , New.DestinationState, New.ShippingType, New.Referral
  INTO @ItemID , @Category, @Price, @Quantity, @OrderDate
  , @DestinationState, @ShippingType, @Referral;
  CALL  CDC_TO_FIREHOSE(@ItemID , @Category, @Price, @Quantity, @OrderDate
  , @DestinationState, @ShippingType, @Referral);
END
;;
DELIMITER ;

If a new row is inserted in the Sales table, the Lambda function that is mentioned in the stored procedure is invoked.

Verify that data is being sent from the Lambda function to Kinesis Data Firehose to Amazon S3 successfully. You might have to insert a few records, depending on the size of your data, before new records appear in Amazon S3. This is due to Kinesis Data Firehose buffering. To learn more about Kinesis Data Firehose buffering, see the “Amazon S3” section in Amazon Kinesis Data Firehose Data Delivery.

Every time a new record is inserted in the sales table, a stored procedure is called, and it updates data in Amazon S3.

Querying data in Amazon Redshift

In this section, you use the data you produced from Amazon Aurora and consume it as-is in Amazon Redshift. In order to allow you to process your data as-is, where it is, while taking advantage of the power and flexibility of Amazon Redshift, you use Amazon Redshift Spectrum. You can use Redshift Spectrum to run complex queries on data stored in Amazon S3, with no need for loading or other data prep.

Just create a data source and issue your queries to your Amazon Redshift cluster as usual. Behind the scenes, Redshift Spectrum scales to thousands of instances on a per-query basis, ensuring that you get fast, consistent performance even as your dataset grows to beyond an exabyte! Being able to query data that is stored in Amazon S3 means that you can scale your compute and your storage independently. You have the full power of the Amazon Redshift query model and all the reporting and business intelligence tools at your disposal. Your queries can reference any combination of data stored in Amazon Redshift tables and in Amazon S3.

Redshift Spectrum supports open, common data types, including CSV/TSV, Apache Parquet, SequenceFile, and RCFile. Files can be compressed using gzip or Snappy, with other data types and compression methods in the works.

First, create an Amazon Redshift cluster. Follow the steps in Launch a Sample Amazon Redshift Cluster.

Next, create an IAM role that has access to Amazon S3 and Athena. By default, Amazon Redshift Spectrum uses the Amazon Athena data catalog. Your cluster needs authorization to access your external data catalog in AWS Glue or Athena and your data files in Amazon S3.

In the demo setup, I attached AmazonS3FullAccess and AmazonAthenaFullAccess. In a production environment, the IAM roles should follow the standard security of granting least privilege. For more information, see IAM Policies for Amazon Redshift Spectrum.

Attach the newly created role to the Amazon Redshift cluster. For more information, see Associate the IAM Role with Your Cluster.

Next, connect to the Amazon Redshift cluster, and create an external schema and database:

create external schema if not exists spectrum_schema
from data catalog 
database 'spectrum_db' 
region 'us-east-1'
IAM_ROLE 'arn:aws:iam::XXXXXXXXXXXX:role/RedshiftSpectrumRole'
create external database if not exists;

Don’t forget to replace the IAM role in the statement.

Then create an external table within the database:

 CREATE EXTERNAL TABLE IF NOT EXISTS spectrum_schema.ecommerce_sales(
  ItemID int,
  Category varchar,
  Price DOUBLE PRECISION,
  Quantity int,
  OrderDate TIMESTAMP,
  DestinationState varchar,
  ShippingType varchar,
  Referral varchar)
ROW FORMAT DELIMITED
      FIELDS TERMINATED BY ','
LINES TERMINATED BY '\n'
LOCATION 's3://{BUCKET_NAME}/CDC/'

Query the table, and it should contain data. This is a fact table.

select top 10 * from spectrum_schema.ecommerce_sales

 

Next, create a dimension table. For this example, we create a date/time dimension table. Create the table:

CREATE TABLE date_dimension (
  d_datekey           integer       not null sortkey,
  d_dayofmonth        integer       not null,
  d_monthnum          integer       not null,
  d_dayofweek                varchar(10)   not null,
  d_prettydate        date       not null,
  d_quarter           integer       not null,
  d_half              integer       not null,
  d_year              integer       not null,
  d_season            varchar(10)   not null,
  d_fiscalyear        integer       not null)
diststyle all;

Populate the table with data:

copy date_dimension from 's3://reparmar-lab/2016dates' 
iam_role 'arn:aws:iam::XXXXXXXXXXXX:role/redshiftspectrum'
DELIMITER ','
dateformat 'auto';

The date dimension table should look like the following:

Querying data in local and external tables using Amazon Redshift

Now that you have the fact and dimension table populated with data, you can combine the two and run analysis. For example, if you want to query the total sales amount by weekday, you can run the following:

select sum(quantity*price) as total_sales, date_dimension.d_season
from spectrum_schema.ecommerce_sales 
join date_dimension on spectrum_schema.ecommerce_sales.orderdate = date_dimension.d_prettydate 
group by date_dimension.d_season

You get the following results:

Similarly, you can replace d_season with d_dayofweek to get sales figures by weekday:

With Amazon Redshift Spectrum, you pay only for the queries you run against the data that you actually scan. We encourage you to use file partitioning, columnar data formats, and data compression to significantly minimize the amount of data scanned in Amazon S3. This is important for data warehousing because it dramatically improves query performance and reduces cost.

Partitioning your data in Amazon S3 by date, time, or any other custom keys enables Amazon Redshift Spectrum to dynamically prune nonrelevant partitions to minimize the amount of data processed. If you store data in a columnar format, such as Parquet, Amazon Redshift Spectrum scans only the columns needed by your query, rather than processing entire rows. Similarly, if you compress your data using one of the supported compression algorithms in Amazon Redshift Spectrum, less data is scanned.

Analyzing and visualizing Amazon Redshift data in Amazon QuickSight

Modify the Amazon Redshift security group to allow an Amazon QuickSight connection. For more information, see Authorizing Connections from Amazon QuickSight to Amazon Redshift Clusters.

After modifying the Amazon Redshift security group, go to Amazon QuickSight. Create a new analysis, and choose Amazon Redshift as the data source.

Enter the database connection details, validate the connection, and create the data source.

Choose the schema to be analyzed. In this case, choose spectrum_schema, and then choose the ecommerce_sales table.

Next, we add a custom field for Total Sales = Price*Quantity. In the drop-down list for the ecommerce_sales table, choose Edit analysis data sets.

On the next screen, choose Edit.

In the data prep screen, choose New Field. Add a new calculated field Total Sales $, which is the product of the Price*Quantity fields. Then choose Create. Save and visualize it.

Next, to visualize total sales figures by month, create a graph with Total Sales on the x-axis and Order Data formatted as month on the y-axis.

After you’ve finished, you can use Amazon QuickSight to add different columns from your Amazon Redshift tables and perform different types of visualizations. You can build operational dashboards that continuously monitor your transactional and analytical data. You can publish these dashboards and share them with others.

Final notes

Amazon QuickSight can also read data in Amazon S3 directly. However, with the method demonstrated in this post, you have the option to manipulate, filter, and combine data from multiple sources or Amazon Redshift tables before visualizing it in Amazon QuickSight.

In this example, we dealt with data being inserted, but triggers can be activated in response to an INSERT, UPDATE, or DELETE trigger.

Keep the following in mind:

  • Be careful when invoking a Lambda function from triggers on tables that experience high write traffic. This would result in a large number of calls to your Lambda function. Although calls to the lambda_async procedure are asynchronous, triggers are synchronous.
  • A statement that results in a large number of trigger activations does not wait for the call to the AWS Lambda function to complete. But it does wait for the triggers to complete before returning control to the client.
  • Similarly, you must account for Amazon Kinesis Data Firehose limits. By default, Kinesis Data Firehose is limited to a maximum of 5,000 records/second. For more information, see Monitoring Amazon Kinesis Data Firehose.

In certain cases, it may be optimal to use AWS Database Migration Service (AWS DMS) to capture data changes in Aurora and use Amazon S3 as a target. For example, AWS DMS might be a good option if you don’t need to transform data from Amazon Aurora. The method used in this post gives you the flexibility to transform data from Aurora using Lambda before sending it to Amazon S3. Additionally, the architecture has the benefits of being serverless, whereas AWS DMS requires an Amazon EC2 instance for replication.

For design considerations while using Redshift Spectrum, see Using Amazon Redshift Spectrum to Query External Data.

If you have questions or suggestions, please comment below.


Additional Reading

If you found this post useful, be sure to check out Capturing Data Changes in Amazon Aurora Using AWS Lambda and 10 Best Practices for Amazon Redshift Spectrum


About the Authors

Re Alvarez-Parmar is a solutions architect for Amazon Web Services. He helps enterprises achieve success through technical guidance and thought leadership. In his spare time, he enjoys spending time with his two kids and exploring outdoors.

 

 

 

Simplify Querying Nested JSON with the AWS Glue Relationalize Transform

Post Syndicated from Trevor Roberts original https://aws.amazon.com/blogs/big-data/simplify-querying-nested-json-with-the-aws-glue-relationalize-transform/

AWS Glue has a transform called Relationalize that simplifies the extract, transform, load (ETL) process by converting nested JSON into columns that you can easily import into relational databases. Relationalize transforms the nested JSON into key-value pairs at the outermost level of the JSON document. The transformed data maintains a list of the original keys from the nested JSON separated by periods.

Let’s look at how Relationalize can help you with a sample use case.

An example of Relationalize in action

Suppose that the developers of a video game want to use a data warehouse like Amazon Redshift to run reports on player behavior based on data that is stored in JSON. Sample 1 shows example user data from the game. The player named “user1” has characteristics such as race, class, and location in nested JSON data. Further down, the player’s arsenal information includes additional nested JSON data. If the developers want to ETL this data into their data warehouse, they might have to resort to nested loops or recursive functions in their code.

Sample 1: Nested JSON

{
	"player": {
		"username": "user1",
		"characteristics": {
			"race": "Human",
			"class": "Warlock",
			"subclass": "Dawnblade",
			"power": 300,
			"playercountry": "USA"
		},
		"arsenal": {
			"kinetic": {
				"name": "Sweet Business",
				"type": "Auto Rifle",
				"power": 300,
				"element": "Kinetic"
			},
			"energy": {
				"name": "MIDA Mini-Tool",
				"type": "Submachine Gun",
				"power": 300,
				"element": "Solar"
			},
			"power": {
				"name": "Play of the Game",
				"type": "Grenade Launcher",
				"power": 300,
				"element": "Arc"
			}
		},
		"armor": {
			"head": "Eye of Another World",
			"arms": "Philomath Gloves",
			"chest": "Philomath Robes",
			"leg": "Philomath Boots",
			"classitem": "Philomath Bond"
		},
		"location": {
			"map": "Titan",
			"waypoint": "The Rig"
		}
	}
}

Instead, the developers can use the Relationalize transform. Sample 2 shows what the transformed data looks like.

Sample 2: Flattened JSON

{
    "player.username": "user1",
    "player.characteristics.race": "Human",
    "player.characteristics.class": "Warlock",
    "player.characteristics.subclass": "Dawnblade",
    "player.characteristics.power": 300,
    "player.characteristics.playercountry": "USA",
    "player.arsenal.kinetic.name": "Sweet Business",
    "player.arsenal.kinetic.type": "Auto Rifle",
    "player.arsenal.kinetic.power": 300,
    "player.arsenal.kinetic.element": "Kinetic",
    "player.arsenal.energy.name": "MIDA Mini-Tool",
    "player.arsenal.energy.type": "Submachine Gun",
    "player.arsenal.energy.power": 300,
    "player.arsenal.energy.element": "Solar",
    "player.arsenal.power.name": "Play of the Game",
    "player.arsenal.power.type": "Grenade Launcher",
    "player.arsenal.power.power": 300,
    "player.arsenal.power.element": "Arc",
    "player.armor.head": "Eye of Another World",
    "player.armor.arms": "Philomath Gloves",
    "player.armor.chest": "Philomath Robes",
    "player.armor.leg": "Philomath Boots",
    "player.armor.classitem": "Philomath Bond",
    "player.location.map": "Titan",
    "player.location.waypoint": "The Rig"
}

You can then write the data to a database or to a data warehouse. You can also write it to delimited text files, such as in comma-separated value (CSV) format, or columnar file formats such as Optimized Row Columnar (ORC) format. You can use either of these format types for long-term storage in Amazon S3. Storing the transformed files in S3 provides the additional benefit of being able to query this data using Amazon Athena or Amazon Redshift Spectrum. You can further extend the usefulness of the data by performing joins between data stored in S3 and the data stored in an Amazon Redshift data warehouse.

Before we get started…

In my example, I took two preparatory steps that save some time in your ETL code development:

  1. I stored my data in an Amazon S3 bucket and used an AWS Glue crawler to make my data available in the AWS Glue data catalog. You can find instructions on how to do that in Cataloging Tables with a Crawler in the AWS Glue documentation. The AWS Glue database name I used was “blog,” and the table name was “players.” You can see these values in use in the sample code that follows.
  2. I deployed a Zeppelin notebook using the automated deployment available within AWS Glue. If you already used an AWS Glue development endpoint to deploy a Zeppelin notebook, you can skip the deployment instructions. Otherwise, let’s quickly review how to deploy Zeppelin.

Deploying a Zeppelin notebook with AWS Glue

The following steps are outlined in the AWS Glue documentation, and I include a few screenshots here for clarity.

First, create two IAM roles:

Next, in the AWS Glue Management Console, choose Dev endpoints, and then choose Add endpoint.

Specify a name for the endpoint and the AWS Glue IAM role that you created.

On the networking screen, choose Skip Networking because our code only communicates with S3.

Complete the development endpoint process by providing a Secure Shell (SSH) public key and confirming your settings.

When your new development endpoint’s Provisioning status changes from PROVISIONING to READY, choose your endpoint, and then for Actions choose Create notebook server.

Enter the notebook server details, including the role you previously created and a security group with inbound access allowed on TCP port 443.

Doing this automatically launches an AWS CloudFormation template. The output specifies the URL that you can use to access your Zeppelin notebook with the username and password you specified in the wizard.

How do we flatten nested JSON?

With my data loaded and my notebook server ready, I accessed Zeppelin, created a new note, and set my interpreter to spark. I used some Python code that AWS Glue previously generated for another job that outputs to ORC. Then I added the Relationalize transform. You can see the resulting Python code in Sample 3.­

Sample 3: Python code to transform the nested JSON and output it to ORC

import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
#from awsglue.transforms import Relationalize

# Begin variables to customize with your information
glue_source_database = "blog"
glue_source_table = "players"
glue_temp_storage = "s3://blog-example-edz/temp"
glue_relationalize_output_s3_path = "s3://blog-example-edz/output-flat"
dfc_root_table_name = "root" #default value is "roottable"
# End variables to customize with your information

glueContext = GlueContext(spark.sparkContext)
datasource0 = glueContext.create_dynamic_frame.from_catalog(database = glue_source_database, table_name = glue_source_table, transformation_ctx = "datasource0")
dfc = Relationalize.apply(frame = datasource0, staging_path = glue_temp_storage, name = dfc_root_table_name, transformation_ctx = "dfc")
blogdata = dfc.select(dfc_root_table_name)
blogdataoutput = glueContext.write_dynamic_frame.from_options(frame = blogdata, connection_type = "s3", connection_options = {"path": glue_relationalize_output_s3_path}, format = "orc", transformation_ctx = "blogdataoutput")

What exactly is going on in this script?

After the import statements, we instantiate a GlueContext object, which allows us to work with the data in AWS Glue. Next, we create a DynamicFrame (datasource0) from the “players” table in the AWS Glue “blog” database. We use this DynamicFrame to perform any necessary operations on the data structure before it’s written to our desired output format. The source files remain unchanged.

We then run the Relationalize transform (Relationalize.apply()) with our datasource0 as one of the parameters. Another important parameter is the name parameter, which is a key that identifies our data after the transformation completes.

The Relationalize.apply() method returns a DynamicFrameCollection, and this is stored in the dfc variable. Before we can write our data to S3, we need to select the DynamicFrame from the DynamicFrameCollection object. We do this with the dfc.select() method. The correct DynamicFrame is stored in the blogdata variable.

You might be curious why a DynamicFrameCollection was returned when we started with a single DynamicFrame. This return value comes from the way Relationalize treats arrays in the JSON document: A DynamicFrame is created for each array. Together with the root data structure, each generated DynamicFrame is added to a DynamicFrameCollection when Relationalize completes its work. Although we didn’t have any arrays in our data, it’s good to keep this in mind. Finally, we output (blogdataoutput) the root DynamicFrame to ORC files in S3.

Using the transformed data

One of the use cases we discussed earlier was using Amazon Athena or Amazon Redshift Spectrum to query the ORC files.

I used the following SQL DDL statements to create external tables in both services to enable queries of my data stored in Amazon S3.

Sample 4: Amazon Athena DDL

CREATE EXTERNAL TABLE IF NOT EXISTS blog.blog_data_athena_test (
  `characteristics_race` string,
  `characteristics_class` string,
  `characteristics_subclass` string,
  `characteristics_power` int,
  `characteristics_playercountry` string,
  `kinetic_name` string,
  `kinetic_type` string,
  `kinetic_power` int,
  `kinetic_element` string,
  `energy_name` string,
  `energy_type` string,
  `energy_power` int,
  `energy_element` string,
  `power_name` string,
  `power_type` string,
  `power_power` int,
  `power_element` string,
  `armor_head` string,
  `armor_arms` string,
  `armor_chest` string,
  `armor_leg` string,
  `armor_classitem` string,
  `map` string,
  `waypoint` string 
)
ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.orc.OrcSerde'
WITH SERDEPROPERTIES (
  'serialization.format' = '1'
) LOCATION 's3://blog-example-edz/output-flat/'
TBLPROPERTIES ('has_encrypted_data'='false');

 

Sample 5: Amazon Redshift Spectrum DDL

-- Create a Schema
-- A single schema can be used with multiple external tables.
-- This step is only required once for the external tables you create.
create external schema spectrum 
from data catalog 
database 'blog' 
iam_role 'arn:aws:iam::0123456789:role/redshift-role'
create external database if not exists;

-- Create an external table in the schema
create external table spectrum.blog(
  username VARCHAR,
  characteristics_race VARCHAR,
  characteristics_class VARCHAR,
  characteristics_subclass VARCHAR,
  characteristics_power INTEGER,
  characteristics_playercountry VARCHAR,
  kinetic_name VARCHAR,
  kinetic_type VARCHAR,
  kinetic_power INTEGER,
  kinetic_element VARCHAR,
  energy_name VARCHAR,
  energy_type VARCHAR,
  energy_power INTEGER,
  energy_element VARCHAR,
  power_name VARCHAR,
  power_type VARCHAR,
  power_power INTEGER,
  power_element VARCHAR,
  armor_head VARCHAR,
  armor_arms VARCHAR,
  armor_chest VARCHAR,
  armor_leg VARCHAR,
  armor_classItem VARCHAR,
  map VARCHAR,
  waypoint VARCHAR)
stored as orc
location 's3://blog-example-edz/output-flat';

I even ran a query, shown in Sample 6, that joined my Redshift Spectrum table (spectrum.playerdata) with data in an Amazon Redshift table (public.raids) to generate advanced reports. In the where clause, I join the two tables based on the username values that are common to both data sources.

Sample 6: Select statement with a join of Redshift Spectrum data with Amazon Redshift data

-- Get Total Raid Completions for the Hunter Class.
select spectrum.playerdata.characteristics_class as class, sum(public.raids."completions.val.raids.leviathan") as "Total Hunter Leviathan Raid Completions" from spectrum.playerdata, public.raids
where spectrum.playerdata.username = public.raids."completions.val.username"
and spectrum.playerdata.characteristics_class = 'Hunter'
group by spectrum.playerdata.characteristics_class;

Summary

This post demonstrated how simple it can be to flatten nested JSON data with AWS Glue, using the Relationalize transform to automate the conversion of nested JSON. AWS Glue also automates the deployment of Zeppelin notebooks that you can use to develop your Python automation script. Finally, AWS Glue can output the transformed data directly to a relational database, or to files in Amazon S3 for further analysis with tools such as Amazon Athena and Amazon Redshift Spectrum.

As great as Relationalize is, it’s not the only transform available with AWS Glue. You can see a complete list of available transforms in Built-In Transforms in the AWS Glue documentation. Try them out today!


Additional Reading

If you found this post useful, be sure to check out Using Amazon Redshift Spectrum, Amazon Athena and AWS Glue with Node.js in Production and Build a Data Lake Foundation with AWS Glue and Amazon S3.


About the Author

Trevor Roberts Jr is a Solutions Architect with AWS. He provides architectural guidance to help customers achieve success in the cloud. In his spare time, Trevor enjoys traveling to new places and spending time with family.

Implementing Dynamic ETL Pipelines Using AWS Step Functions

Post Syndicated from Tara Van Unen original https://aws.amazon.com/blogs/compute/implementing-dynamic-etl-pipelines-using-aws-step-functions/

This post contributed by:
Wangechi Dole, AWS Solutions Architect
Milan Krasnansky, ING, Digital Solutions Developer, SGK
Rian Mookencherry, Director – Product Innovation, SGK

Data processing and transformation is a common use case you see in our customer case studies and success stories. Often, customers deal with complex data from a variety of sources that needs to be transformed and customized through a series of steps to make it useful to different systems and stakeholders. This can be difficult due to the ever-increasing volume, velocity, and variety of data. Today, data management challenges cannot be solved with traditional databases.

Workflow automation helps you build solutions that are repeatable, scalable, and reliable. You can use AWS Step Functions for this. A great example is how SGK used Step Functions to automate the ETL processes for their client. With Step Functions, SGK has been able to automate changes within the data management system, substantially reducing the time required for data processing.

In this post, SGK shares the details of how they used Step Functions to build a robust data processing system based on highly configurable business transformation rules for ETL processes.

SGK: Building dynamic ETL pipelines

SGK is a subsidiary of Matthews International Corporation, a diversified organization focusing on brand solutions and industrial technologies. SGK’s Global Content Creation Studio network creates compelling content and solutions that connect brands and products to consumers through multiple assets including photography, video, and copywriting.

We were recently contracted to build a sophisticated and scalable data management system for one of our clients. We chose to build the solution on AWS to leverage advanced, managed services that help to improve the speed and agility of development.

The data management system served two main functions:

  1. Ingesting a large amount of complex data to facilitate both reporting and product funding decisions for the client’s global marketing and supply chain organizations.
  2. Processing the data through normalization and applying complex algorithms and data transformations. The system goal was to provide information in the relevant context—such as strategic marketing, supply chain, product planning, etc. —to the end consumer through automated data feeds or updates to existing ETL systems.

We were faced with several challenges:

  • Output data that needed to be refreshed at least twice a day to provide fresh datasets to both local and global markets. That constant data refresh posed several challenges, especially around data management and replication across multiple databases.
  • The complexity of reporting business rules that needed to be updated on a constant basis.
  • Data that could not be processed as contiguous blocks of typical time-series data. The measurement of the data was done across seasons (that is, combination of dates), which often resulted with up to three overlapping seasons at any given time.
  • Input data that came from 10+ different data sources. Each data source ranged from 1–20K rows with as many as 85 columns per input source.

These challenges meant that our small Dev team heavily invested time in frequent configuration changes to the system and data integrity verification to make sure that everything was operating properly. Maintaining this system proved to be a daunting task and that’s when we turned to Step Functions—along with other AWS services—to automate our ETL processes.

Solution overview

Our solution included the following AWS services:

  • AWS Step Functions: Before Step Functions was available, we were using multiple Lambda functions for this use case and running into memory limit issues. With Step Functions, we can execute steps in parallel simultaneously, in a cost-efficient manner, without running into memory limitations.
  • AWS Lambda: The Step Functions state machine uses Lambda functions to implement the Task states. Our Lambda functions are implemented in Java 8.
  • Amazon DynamoDB provides us with an easy and flexible way to manage business rules. We specify our rules as Keys. These are key-value pairs stored in a DynamoDB table.
  • Amazon RDS: Our ETL pipelines consume source data from our RDS MySQL database.
  • Amazon Redshift: We use Amazon Redshift for reporting purposes because it integrates with our BI tools. Currently we are using Tableau for reporting which integrates well with Amazon Redshift.
  • Amazon S3: We store our raw input files and intermediate results in S3 buckets.
  • Amazon CloudWatch Events: Our users expect results at a specific time. We use CloudWatch Events to trigger Step Functions on an automated schedule.

Solution architecture

This solution uses a declarative approach to defining business transformation rules that are applied by the underlying Step Functions state machine as data moves from RDS to Amazon Redshift. An S3 bucket is used to store intermediate results. A CloudWatch Event rule triggers the Step Functions state machine on a schedule. The following diagram illustrates our architecture:

Here are more details for the above diagram:

  1. A rule in CloudWatch Events triggers the state machine execution on an automated schedule.
  2. The state machine invokes the first Lambda function.
  3. The Lambda function deletes all existing records in Amazon Redshift. Depending on the dataset, the Lambda function can create a new table in Amazon Redshift to hold the data.
  4. The same Lambda function then retrieves Keys from a DynamoDB table. Keys represent specific marketing campaigns or seasons and map to specific records in RDS.
  5. The state machine executes the second Lambda function using the Keys from DynamoDB.
  6. The second Lambda function retrieves the referenced dataset from RDS. The records retrieved represent the entire dataset needed for a specific marketing campaign.
  7. The second Lambda function executes in parallel for each Key retrieved from DynamoDB and stores the output in CSV format temporarily in S3.
  8. Finally, the Lambda function uploads the data into Amazon Redshift.

To understand the above data processing workflow, take a closer look at the Step Functions state machine for this example.

We walk you through the state machine in more detail in the following sections.

Walkthrough

To get started, you need to:

  • Create a schedule in CloudWatch Events
  • Specify conditions for RDS data extracts
  • Create Amazon Redshift input files
  • Load data into Amazon Redshift

Step 1: Create a schedule in CloudWatch Events
Create rules in CloudWatch Events to trigger the Step Functions state machine on an automated schedule. The following is an example cron expression to automate your schedule:

In this example, the cron expression invokes the Step Functions state machine at 3:00am and 2:00pm (UTC) every day.

Step 2: Specify conditions for RDS data extracts
We use DynamoDB to store Keys that determine which rows of data to extract from our RDS MySQL database. An example Key is MCS2017, which stands for, Marketing Campaign Spring 2017. Each campaign has a specific start and end date and the corresponding dataset is stored in RDS MySQL. A record in RDS contains about 600 columns, and each Key can represent up to 20K records.

A given day can have multiple campaigns with different start and end dates running simultaneously. In the following example DynamoDB item, three campaigns are specified for the given date.

The state machine example shown above uses Keys 31, 32, and 33 in the first ChoiceState and Keys 21 and 22 in the second ChoiceState. These keys represent marketing campaigns for a given day. For example, on Monday, there are only two campaigns requested. The ChoiceState with Keys 21 and 22 is executed. If three campaigns are requested on Tuesday, for example, then ChoiceState with Keys 31, 32, and 33 is executed. MCS2017 can be represented by Key 21 and Key 33 on Monday and Tuesday, respectively. This approach gives us the flexibility to add or remove campaigns dynamically.

Step 3: Create Amazon Redshift input files
When the state machine begins execution, the first Lambda function is invoked as the resource for FirstState, represented in the Step Functions state machine as follows:

"Comment": ” AWS Amazon States Language.", 
  "StartAt": "FirstState",
 
"States": { 
  "FirstState": {
   
"Type": "Task",
   
"Resource": "arn:aws:lambda:xx-xxxx-x:XXXXXXXXXXXX:function:Start",
    "Next": "ChoiceState" 
  } 

As described in the solution architecture, the purpose of this Lambda function is to delete existing data in Amazon Redshift and retrieve keys from DynamoDB. In our use case, we found that deleting existing records was more efficient and less time-consuming than finding the delta and updating existing records. On average, an Amazon Redshift table can contain about 36 million cells, which translates to roughly 65K records. The following is the code snippet for the first Lambda function in Java 8:

public class LambdaFunctionHandler implements RequestHandler<Map<String,Object>,Map<String,String>> {
    Map<String,String> keys= new HashMap<>();
    public Map<String, String> handleRequest(Map<String, Object> input, Context context){
       Properties config = getConfig(); 
       // 1. Cleaning Redshift Database
       new RedshiftDataService(config).cleaningTable(); 
       // 2. Reading data from Dynamodb
       List<String> keyList = new DynamoDBDataService(config).getCurrentKeys();
       for(int i = 0; i < keyList.size(); i++) {
           keys.put(”key" + (i+1), keyList.get(i)); 
       }
       keys.put(”key" + T,String.valueOf(keyList.size()));
       // 3. Returning the key values and the key count from the “for” loop
       return (keys);
}

The following JSON represents ChoiceState.

"ChoiceState": {
   "Type" : "Choice",
   "Choices": [ 
   {

      "Variable": "$.keyT",
     "StringEquals": "3",
     "Next": "CurrentThreeKeys" 
   }, 
   {

     "Variable": "$.keyT",
    "StringEquals": "2",
    "Next": "CurrentTwooKeys" 
   } 
 ], 
 "Default": "DefaultState"
}

The variable $.keyT represents the number of keys retrieved from DynamoDB. This variable determines which of the parallel branches should be executed. At the time of publication, Step Functions does not support dynamic parallel state. Therefore, choices under ChoiceState are manually created and assigned hardcoded StringEquals values. These values represent the number of parallel executions for the second Lambda function.

For example, if $.keyT equals 3, the second Lambda function is executed three times in parallel with keys, $key1, $key2 and $key3 retrieved from DynamoDB. Similarly, if $.keyT equals two, the second Lambda function is executed twice in parallel.  The following JSON represents this parallel execution:

"CurrentThreeKeys": { 
  "Type": "Parallel",
  "Next": "NextState",
  "Branches": [ 
  {

     "StartAt": “key31",
    "States": { 
       “key31": {

          "Type": "Task",
        "InputPath": "$.key1",
        "Resource": "arn:aws:lambda:xx-xxxx-x:XXXXXXXXXXXX:function:Execution",
        "End": true 
       } 
    } 
  }, 
  {

     "StartAt": “key32",
    "States": { 
     “key32": {

        "Type": "Task",
       "InputPath": "$.key2",
         "Resource": "arn:aws:lambda:xx-xxxx-x:XXXXXXXXXXXX:function:Execution",
       "End": true 
      } 
     } 
   }, 
   {

      "StartAt": “key33",
       "States": { 
          “key33": {

                "Type": "Task",
             "InputPath": "$.key3",
             "Resource": "arn:aws:lambda:xx-xxxx-x:XXXXXXXXXXXX:function:Execution",
           "End": true 
       } 
     } 
    } 
  ] 
} 

Step 4: Load data into Amazon Redshift
The second Lambda function in the state machine extracts records from RDS associated with keys retrieved for DynamoDB. It processes the data then loads into an Amazon Redshift table. The following is code snippet for the second Lambda function in Java 8.

public class LambdaFunctionHandler implements RequestHandler<String, String> {
 public static String key = null;

public String handleRequest(String input, Context context) { 
   key=input; 
   //1. Getting basic configurations for the next classes + s3 client Properties
   config = getConfig();

   AmazonS3 s3 = AmazonS3ClientBuilder.defaultClient(); 
   // 2. Export query results from RDS into S3 bucket 
   new RdsDataService(config).exportDataToS3(s3,key); 
   // 3. Import query results from S3 bucket into Redshift 
    new RedshiftDataService(config).importDataFromS3(s3,key); 
   System.out.println(input); 
   return "SUCCESS"; 
 } 
}

After the data is loaded into Amazon Redshift, end users can visualize it using their preferred business intelligence tools.

Lessons learned

  • At the time of publication, the 1.5–GB memory hard limit for Lambda functions was inadequate for processing our complex workload. Step Functions gave us the flexibility to chunk our large datasets and process them in parallel, saving on costs and time.
  • In our previous implementation, we assigned each key a dedicated Lambda function along with CloudWatch rules for schedule automation. This approach proved to be inefficient and quickly became an operational burden. Previously, we processed each key sequentially, with each key adding about five minutes to the overall processing time. For example, processing three keys meant that the total processing time was three times longer. With Step Functions, the entire state machine executes in about five minutes.
  • Using DynamoDB with Step Functions gave us the flexibility to manage keys efficiently. In our previous implementations, keys were hardcoded in Lambda functions, which became difficult to manage due to frequent updates. DynamoDB is a great way to store dynamic data that changes frequently, and it works perfectly with our serverless architectures.

Conclusion

With Step Functions, we were able to fully automate the frequent configuration updates to our dataset resulting in significant cost savings, reduced risk to data errors due to system downtime, and more time for us to focus on new product development rather than support related issues. We hope that you have found the information useful and that it can serve as a jump-start to building your own ETL processes on AWS with managed AWS services.

For more information about how Step Functions makes it easy to coordinate the components of distributed applications and microservices in any workflow, see the use case examples and then build your first state machine in under five minutes in the Step Functions console.

If you have questions or suggestions, please comment below.

Collect Data Statistics Up to 5x Faster by Analyzing Only Predicate Columns with Amazon Redshift

Post Syndicated from George Caragea original https://aws.amazon.com/blogs/big-data/collect-data-statistics-up-to-5x-faster-by-analyzing-only-predicate-columns-with-amazon-redshift/

Amazon Redshift is a fast, fully managed, petabyte-scale data warehousing service that makes it simple and cost-effective to analyze all of your data. Many of our customers—including Boingo Wireless, Scholastic, Finra, Pinterest, and Foursquare—migrated to Amazon Redshift and achieved agility and faster time to insight, while dramatically reducing costs.

Query optimization and the need for accurate estimates

When a SQL query is submitted to Amazon Redshift, the query optimizer is in charge of generating all the possible ways to execute that query, and picking the fastest one. This can mean evaluating the cost of thousands, if not millions, of different execution plans.

The plan cost is calculated based on estimates of the data characteristics. For example, the characteristics could include the number of rows in each base table, the average width of a variable-length column, the number of distinct values in a column, and the most common values in a column. These estimates (or “statistics”) are computed in advance by running an ANALYZE command, and stored in the system catalog.

How do the query optimizer and ANALYZE work together?

An ideal scenario is to run ANALYZE after every ETL/ingestion job. This way, when running your workload, the query optimizer can use up-to-date data statistics, and choose the most optimal execution plan, given the updates.

However, running the ANALYZE command can add significant overhead to the data ingestion scripts. This can lead to customers not running ANALYZE on their data, and using default or stale estimates. The end result is usually the optimizer choosing a suboptimal execution plan that runs for longer than needed.

Analyzing predicate columns only

When you run a SQL query, the query optimizer requests statistics only on columns used in predicates in the SQL query (join predicates, filters in the WHERE clause and GROUP BY clauses). Consider the following query:

SELECT Avg(salary), 
       Min(hiredate), 
       deptname 
FROM   emp 
WHERE  state = 'CA' 
GROUP  BY deptname; 

In the query above, the optimizer requests statistics only on columns ‘state’ and ‘deptname’, but not on ‘salary’ and ‘hiredate’. If present, statistics on columns ‘salary’ and ‘hiredate’ are ignored, as they do not impact the cost of the execution plans considered.

Based on the optimizer functionality described earlier, the Amazon Redshift ANALYZE command has been updated to optionally collect information only about columns used in previous queries as part of a filter, join condition or a GROUP BY clause, and columns that are part of distribution or sort keys (predicate columns). There’s a recently introduced option for the ANALYZE command that only analyzes predicate columns:

ANALYZE <table name> PREDICATE COLUMNS;

By having Amazon Redshift collect information about predicate columns automatically, and analyzing those columns only, you’re able to reduce the time to run ANALYZE. For example, during the execution of the 99 queries in the TPC-DS workload, only 203 out of the 424 total columns are predicate columns (approximately 48%). By analyzing only the predicate columns for such a workload, the execution time for running ANALYZE can be significantly reduced.

From my experience in the data warehousing space, I have observed that about 20% of columns in a typical use case are marked predicate. In such a case, running ANALYZE PREDICATE COLUMNS can lead to a speedup of up to 5x relative to a full ANALYZE run.

If no information on predicate columns exists in the system (for example, a new table that has not been queried yet), ANALYZE PREDICATE COLUMNS collects statistics on all the columns. When queries on the table are run, Amazon Redshift collects information about predicate column usage, and subsequent runs of ANALYZE PREDICATE COLUMNS only operates on the predicate columns.

If the workload is relatively stable, and the set of predicate columns does not expand continuously over time, I recommend replacing all occurrences of the ANALYZE command with ANALYZE PREDICATE COLUMNS commands in your application and data ingestion code.

Using the Analyze/Vacuum utility

Several AWS customers are using the Analyze/Vacuum utility from the Redshift-Utils package to manage and automate their maintenance operations. By passing the –predicate-cols option to the Analyze/Vacuum utility, you can enable it to use the ANALYZE PREDICATE COLUMNS feature, providing you with the significant changes in overhead in a completely seamless manner.

Enhancements to logging for ANALYZE operations

When running ANALYZE with the PREDICATE COLUMNS option, the type of analyze run (Full vs Predicate Column), as well as information about the predicate columns encountered, is logged in the stl_analyze view:

SELECT status, 
       starttime, 
       prevtime, 
       num_predicate_cols, 
       num_new_predicate_cols 
FROM   stl_analyze;
     status   |    starttime        |   prevtime          | pred_cols | new_pred_cols
--------------+---------------------+---------------------+-----------+---------------
 Full         | 2017-11-09 01:15:47 |                     |         0 |             0
 PredicateCol | 2017-11-09 01:16:20 | 2017-11-09 01:15:47 |         2 |             2

AWS also enhanced the pg_statistic catalog table with two new pieces of information: the time stamp at which a column was marked as “predicate”, and the time stamp at which the column was last analyzed.

The Amazon Redshift documentation provides a view that allows a user to easily see which columns are marked as predicate, when they were marked as predicate, and when a column was last analyzed. For example, for the emp table used above, the output of the view could be as follows:

 SELECT col_name, 
       is_predicate, 
       first_predicate_use, 
       last_analyze 
FROM   predicate_columns 
WHERE  table_name = 'emp';

 col_name | is_predicate | first_predicate_use  |        last_analyze
----------+--------------+----------------------+----------------------------
 id       | f            |                      | 2017-11-09 01:15:47
 name     | f            |                      | 2017-11-09 01:15:47
 deptname | t            | 2017-11-09 01:16:03  | 2017-11-09 01:16:20
 age      | f            |                      | 2017-11-09 01:15:47
 salary   | f            |                      | 2017-11-09 01:15:47
 hiredate | f            |                      | 2017-11-09 01:15:47
 state    | t            | 2017-11-09 01:16:03  | 2017-11-09 01:16:20

Conclusion

After loading new data into an Amazon Redshift cluster, statistics need to be re-computed to guarantee performant query plans. By learning which column statistics are actually being used by the customer’s workload and collecting statistics only on those columns, Amazon Redshift is able to significantly reduce the amount of time needed for table maintenance during data loading workflows.


Additional Reading

Be sure to check out the Top 10 Tuning Techniques for Amazon Redshift, and the Advanced Table Design Playbook: Distribution Styles and Distribution Keys.


About the Author

George Caragea is a Senior Software Engineer with Amazon Redshift. He has been working on MPP Databases for over 6 years and is mainly interested in designing systems at scale. In his spare time, he enjoys being outdoors and on the water in the beautiful Bay Area and finishing the day exploring the rich local restaurant scene.

 

 

GDPR – A Practical Guide For Developers

Post Syndicated from Bozho original https://techblog.bozho.net/gdpr-practical-guide-developers/

You’ve probably heard about GDPR. The new European data protection regulation that applies practically to everyone. Especially if you are working in a big company, it’s most likely that there’s already a process for gettign your systems in compliance with the regulation.

The regulation is basically a law that must be followed in all European countries (but also applies to non-EU companies that have users in the EU). In this particular case, it applies to companies that are not registered in Europe, but are having European customers. So that’s most companies. I will not go into yet another “12 facts about GDPR” or “7 myths about GDPR” posts/whitepapers, as they are often aimed at managers or legal people. Instead, I’ll focus on what GDPR means for developers.

Why am I qualified to do that? A few reasons – I was advisor to the deputy prime minister of a EU country, and because of that I’ve been both exposed and myself wrote some legislation. I’m familiar with the “legalese” and how the regulatory framework operates in general. I’m also a privacy advocate and I’ve been writing about GDPR-related stuff in the past, i.e. “before it was cool” (protecting sensitive data, the right to be forgotten). And finally, I’m currently working on a project that (among other things) aims to help with covering some GDPR aspects.

I’ll try to be a bit more comprehensive this time and cover as many aspects of the regulation that concern developers as I can. And while developers will mostly be concerned about how the systems they are working on have to change, it’s not unlikely that a less informed manager storms in in late spring, realizing GDPR is going to be in force tomorrow, asking “what should we do to get our system/website compliant”.

The rights of the user/client (referred to as “data subject” in the regulation) that I think are relevant for developers are: the right to erasure (the right to be forgotten/deleted from the system), right to restriction of processing (you still keep the data, but mark it as “restricted” and don’t touch it without further consent by the user), the right to data portability (the ability to export one’s data), the right to rectification (the ability to get personal data fixed), the right to be informed (getting human-readable information, rather than long terms and conditions), the right of access (the user should be able to see all the data you have about them), the right to data portability (the user should be able to get a machine-readable dump of their data).

Additionally, the relevant basic principles are: data minimization (one should not collect more data than necessary), integrity and confidentiality (all security measures to protect data that you can think of + measures to guarantee that the data has not been inappropriately modified).

Even further, the regulation requires certain processes to be in place within an organization (of more than 250 employees or if a significant amount of data is processed), and those include keeping a record of all types of processing activities carried out, including transfers to processors (3rd parties), which includes cloud service providers. None of the other requirements of the regulation have an exception depending on the organization size, so “I’m small, GDPR does not concern me” is a myth.

It is important to know what “personal data” is. Basically, it’s every piece of data that can be used to uniquely identify a person or data that is about an already identified person. It’s data that the user has explicitly provided, but also data that you have collected about them from either 3rd parties or based on their activities on the site (what they’ve been looking at, what they’ve purchased, etc.)

Having said that, I’ll list a number of features that will have to be implemented and some hints on how to do that, followed by some do’s and don’t’s.

  • “Forget me” – you should have a method that takes a userId and deletes all personal data about that user (in case they have been collected on the basis of consent, and not due to contract enforcement or legal obligation). It is actually useful for integration tests to have that feature (to cleanup after the test), but it may be hard to implement depending on the data model. In a regular data model, deleting a record may be easy, but some foreign keys may be violated. That means you have two options – either make sure you allow nullable foreign keys (for example an order usually has a reference to the user that made it, but when the user requests his data be deleted, you can set the userId to null), or make sure you delete all related data (e.g. via cascades). This may not be desirable, e.g. if the order is used to track available quantities or for accounting purposes. It’s a bit trickier for event-sourcing data models, or in extreme cases, ones that include some sort of blcokchain/hash chain/tamper-evident data structure. With event sourcing you should be able to remove a past event and re-generate intermediate snapshots. For blockchain-like structures – be careful what you put in there and avoid putting personal data of users. There is an option to use a chameleon hash function, but that’s suboptimal. Overall, you must constantly think of how you can delete the personal data. And “our data model doesn’t allow it” isn’t an excuse.
  • Notify 3rd parties for erasure – deleting things from your system may be one thing, but you are also obligated to inform all third parties that you have pushed that data to. So if you have sent personal data to, say, Salesforce, Hubspot, twitter, or any cloud service provider, you should call an API of theirs that allows for the deletion of personal data. If you are such a provider, obviously, your “forget me” endpoint should be exposed. Calling the 3rd party APIs to remove data is not the full story, though. You also have to make sure the information does not appear in search results. Now, that’s tricky, as Google doesn’t have an API for removal, only a manual process. Fortunately, it’s only about public profile pages that are crawlable by Google (and other search engines, okay…), but you still have to take measures. Ideally, you should make the personal data page return a 404 HTTP status, so that it can be removed.
  • Restrict processing – in your admin panel where there’s a list of users, there should be a button “restrict processing”. The user settings page should also have that button. When clicked (after reading the appropriate information), it should mark the profile as restricted. That means it should no longer be visible to the backoffice staff, or publicly. You can implement that with a simple “restricted” flag in the users table and a few if-clasues here and there.
  • Export data – there should be another button – “export data”. When clicked, the user should receive all the data that you hold about them. What exactly is that data – depends on the particular usecase. Usually it’s at least the data that you delete with the “forget me” functionality, but may include additional data (e.g. the orders the user has made may not be delete, but should be included in the dump). The structure of the dump is not strictly defined, but my recommendation would be to reuse schema.org definitions as much as possible, for either JSON or XML. If the data is simple enough, a CSV/XLS export would also be fine. Sometimes data export can take a long time, so the button can trigger a background process, which would then notify the user via email when his data is ready (twitter, for example, does that already – you can request all your tweets and you get them after a while).
  • Allow users to edit their profile – this seems an obvious rule, but it isn’t always followed. Users must be able to fix all data about them, including data that you have collected from other sources (e.g. using a “login with facebook” you may have fetched their name and address). Rule of thumb – all the fields in your “users” table should be editable via the UI. Technically, rectification can be done via a manual support process, but that’s normally more expensive for a business than just having the form to do it. There is one other scenario, however, when you’ve obtained the data from other sources (i.e. the user hasn’t provided their details to you directly). In that case there should still be a page where they can identify somehow (via email and/or sms confirmation) and get access to the data about them.
  • Consent checkboxes – this is in my opinion the biggest change that the regulation brings. “I accept the terms and conditions” would no longer be sufficient to claim that the user has given their consent for processing their data. So, for each particular processing activity there should be a separate checkbox on the registration (or user profile) screen. You should keep these consent checkboxes in separate columns in the database, and let the users withdraw their consent (by unchecking these checkboxes from their profile page – see the previous point). Ideally, these checkboxes should come directly from the register of processing activities (if you keep one). Note that the checkboxes should not be preselected, as this does not count as “consent”.
  • Re-request consent – if the consent users have given was not clear (e.g. if they simply agreed to terms & conditions), you’d have to re-obtain that consent. So prepare a functionality for mass-emailing your users to ask them to go to their profile page and check all the checkboxes for the personal data processing activities that you have.
  • “See all my data” – this is very similar to the “Export” button, except data should be displayed in the regular UI of the application rather than an XML/JSON format. For example, Google Maps shows you your location history – all the places that you’ve been to. It is a good implementation of the right to access. (Though Google is very far from perfect when privacy is concerned)
  • Age checks – you should ask for the user’s age, and if the user is a child (below 16), you should ask for parent permission. There’s no clear way how to do that, but my suggestion is to introduce a flow, where the child should specify the email of a parent, who can then confirm. Obviosuly, children will just cheat with their birthdate, or provide a fake parent email, but you will most likely have done your job according to the regulation (this is one of the “wishful thinking” aspects of the regulation).

Now some “do’s”, which are mostly about the technical measures needed to protect personal data. They may be more “ops” than “dev”, but often the application also has to be extended to support them. I’ve listed most of what I could think of in a previous post.

  • Encrypt the data in transit. That means that communication between your application layer and your database (or your message queue, or whatever component you have) should be over TLS. The certificates could be self-signed (and possibly pinned), or you could have an internal CA. Different databases have different configurations, just google “X encrypted connections. Some databases need gossiping among the nodes – that should also be configured to use encryption
  • Encrypt the data at rest – this again depends on the database (some offer table-level encryption), but can also be done on machine-level. E.g. using LUKS. The private key can be stored in your infrastructure, or in some cloud service like AWS KMS.
  • Encrypt your backups – kind of obvious
  • Implement pseudonymisation – the most obvious use-case is when you want to use production data for the test/staging servers. You should change the personal data to some “pseudonym”, so that the people cannot be identified. When you push data for machine learning purposes (to third parties or not), you can also do that. Technically, that could mean that your User object can have a “pseudonymize” method which applies hash+salt/bcrypt/PBKDF2 for some of the data that can be used to identify a person
  • Protect data integrity – this is a very broad thing, and could simply mean “have authentication mechanisms for modifying data”. But you can do something more, even as simple as a checksum, or a more complicated solution (like the one I’m working on). It depends on the stakes, on the way data is accessed, on the particular system, etc. The checksum can be in the form of a hash of all the data in a given database record, which should be updated each time the record is updated through the application. It isn’t a strong guarantee, but it is at least something.
  • Have your GDPR register of processing activities in something other than Excel – Article 30 says that you should keep a record of all the types of activities that you use personal data for. That sounds like bureaucracy, but it may be useful – you will be able to link certain aspects of your application with that register (e.g. the consent checkboxes, or your audit trail records). It wouldn’t take much time to implement a simple register, but the business requirements for that should come from whoever is responsible for the GDPR compliance. But you can advise them that having it in Excel won’t make it easy for you as a developer (imagine having to fetch the excel file internally, so that you can parse it and implement a feature). Such a register could be a microservice/small application deployed separately in your infrastructure.
  • Log access to personal data – every read operation on a personal data record should be logged, so that you know who accessed what and for what purpose
  • Register all API consumers – you shouldn’t allow anonymous API access to personal data. I’d say you should request the organization name and contact person for each API user upon registration, and add those to the data processing register. Note: some have treated article 30 as a requirement to keep an audit log. I don’t think it is saying that – instead it requires 250+ companies to keep a register of the types of processing activities (i.e. what you use the data for). There are other articles in the regulation that imply that keeping an audit log is a best practice (for protecting the integrity of the data as well as to make sure it hasn’t been processed without a valid reason)

Finally, some “don’t’s”.

  • Don’t use data for purposes that the user hasn’t agreed with – that’s supposed to be the spirit of the regulation. If you want to expose a new API to a new type of clients, or you want to use the data for some machine learning, or you decide to add ads to your site based on users’ behaviour, or sell your database to a 3rd party – think twice. I would imagine your register of processing activities could have a button to send notification emails to users to ask them for permission when a new processing activity is added (or if you use a 3rd party register, it should probably give you an API). So upon adding a new processing activity (and adding that to your register), mass email all users from whom you’d like consent.
  • Don’t log personal data – getting rid of the personal data from log files (especially if they are shipped to a 3rd party service) can be tedious or even impossible. So log just identifiers if needed. And make sure old logs files are cleaned up, just in case
  • Don’t put fields on the registration/profile form that you don’t need – it’s always tempting to just throw as many fields as the usability person/designer agrees on, but unless you absolutely need the data for delivering your service, you shouldn’t collect it. Names you should probably always collect, but unless you are delivering something, a home address or phone is unnecessary.
  • Don’t assume 3rd parties are compliant – you are responsible if there’s a data breach in one of the 3rd parties (e.g. “processors”) to which you send personal data. So before you send data via an API to another service, make sure they have at least a basic level of data protection. If they don’t, raise a flag with management.
  • Don’t assume having ISO XXX makes you compliant – information security standards and even personal data standards are a good start and they will probably 70% of what the regulation requires, but they are not sufficient – most of the things listed above are not covered in any of those standards

Overall, the purpose of the regulation is to make you take conscious decisions when processing personal data. It imposes best practices in a legal way. If you follow the above advice and design your data model, storage, data flow , API calls with data protection in mind, then you shouldn’t worry about the huge fines that the regulation prescribes – they are for extreme cases, like Equifax for example. Regulators (data protection authorities) will most likely have some checklists into which you’d have to somehow fit, but if you follow best practices, that shouldn’t be an issue.

I think all of the above features can be implemented in a few weeks by a small team. Be suspicious when a big vendor offers you a generic plug-and-play “GDPR compliance” solution. GDPR is not just about the technical aspects listed above – it does have organizational/process implications. But also be suspicious if a consultant claims GDPR is complicated. It’s not – it relies on a few basic principles that are in fact best practices anyway. Just don’t ignore them.

The post GDPR – A Practical Guide For Developers appeared first on Bozho's tech blog.

Using Amazon Redshift Spectrum, Amazon Athena, and AWS Glue with Node.js in Production

Post Syndicated from Rafi Ton original https://aws.amazon.com/blogs/big-data/using-amazon-redshift-spectrum-amazon-athena-and-aws-glue-with-node-js-in-production/

This is a guest post by Rafi Ton, founder and CEO of NUVIAD. NUVIAD is, in their own words, “a mobile marketing platform providing professional marketers, agencies and local businesses state of the art tools to promote their products and services through hyper targeting, big data analytics and advanced machine learning tools.”

At NUVIAD, we’ve been using Amazon Redshift as our main data warehouse solution for more than 3 years.

We store massive amounts of ad transaction data that our users and partners analyze to determine ad campaign strategies. When running real-time bidding (RTB) campaigns in large scale, data freshness is critical so that our users can respond rapidly to changes in campaign performance. We chose Amazon Redshift because of its simplicity, scalability, performance, and ability to load new data in near real time.

Over the past three years, our customer base grew significantly and so did our data. We saw our Amazon Redshift cluster grow from three nodes to 65 nodes. To balance cost and analytics performance, we looked for a way to store large amounts of less-frequently analyzed data at a lower cost. Yet, we still wanted to have the data immediately available for user queries and to meet their expectations for fast performance. We turned to Amazon Redshift Spectrum.

In this post, I explain the reasons why we extended Amazon Redshift with Redshift Spectrum as our modern data warehouse. I cover how our data growth and the need to balance cost and performance led us to adopt Redshift Spectrum. I also share key performance metrics in our environment, and discuss the additional AWS services that provide a scalable and fast environment, with data available for immediate querying by our growing user base.

Amazon Redshift as our foundation

The ability to provide fresh, up-to-the-minute data to our customers and partners was always a main goal with our platform. We saw other solutions provide data that was a few hours old, but this was not good enough for us. We insisted on providing the freshest data possible. For us, that meant loading Amazon Redshift in frequent micro batches and allowing our customers to query Amazon Redshift directly to get results in near real time.

The benefits were immediately evident. Our customers could see how their campaigns performed faster than with other solutions, and react sooner to the ever-changing media supply pricing and availability. They were very happy.

However, this approach required Amazon Redshift to store a lot of data for long periods, and our data grew substantially. In our peak, we maintained a cluster running 65 DC1.large nodes. The impact on our Amazon Redshift cluster was evident, and we saw our CPU utilization grow to 90%.

Why we extended Amazon Redshift to Redshift Spectrum

Redshift Spectrum gives us the ability to run SQL queries using the powerful Amazon Redshift query engine against data stored in Amazon S3, without needing to load the data. With Redshift Spectrum, we store data where we want, at the cost that we want. We have the data available for analytics when our users need it with the performance they expect.

Seamless scalability, high performance, and unlimited concurrency

Scaling Redshift Spectrum is a simple process. First, it allows us to leverage Amazon S3 as the storage engine and get practically unlimited data capacity.

Second, if we need more compute power, we can leverage Redshift Spectrum’s distributed compute engine over thousands of nodes to provide superior performance – perfect for complex queries running against massive amounts of data.

Third, all Redshift Spectrum clusters access the same data catalog so that we don’t have to worry about data migration at all, making scaling effortless and seamless.

Lastly, since Redshift Spectrum distributes queries across potentially thousands of nodes, they are not affected by other queries, providing much more stable performance and unlimited concurrency.

Keeping it SQL

Redshift Spectrum uses the same query engine as Amazon Redshift. This means that we did not need to change our BI tools or query syntax, whether we used complex queries across a single table or joins across multiple tables.

An interesting capability introduced recently is the ability to create a view that spans both Amazon Redshift and Redshift Spectrum external tables. With this feature, you can query frequently accessed data in your Amazon Redshift cluster and less-frequently accessed data in Amazon S3, using a single view.

Leveraging Parquet for higher performance

Parquet is a columnar data format that provides superior performance and allows Redshift Spectrum (or Amazon Athena) to scan significantly less data. With less I/O, queries run faster and we pay less per query. You can read all about Parquet at https://parquet.apache.org/ or https://en.wikipedia.org/wiki/Apache_Parquet.

Lower cost

From a cost perspective, we pay standard rates for our data in Amazon S3, and only small amounts per query to analyze data with Redshift Spectrum. Using the Parquet format, we can significantly reduce the amount of data scanned. Our costs are now lower, and our users get fast results even for large complex queries.

What we learned about Amazon Redshift vs. Redshift Spectrum performance

When we first started looking at Redshift Spectrum, we wanted to put it to the test. We wanted to know how it would compare to Amazon Redshift, so we looked at two key questions:

  1. What is the performance difference between Amazon Redshift and Redshift Spectrum on simple and complex queries?
  2. Does the data format impact performance?

During the migration phase, we had our dataset stored in Amazon Redshift and S3 as CSV/GZIP and as Parquet file formats. We tested three configurations:

  • Amazon Redshift cluster with 28 DC1.large nodes
  • Redshift Spectrum using CSV/GZIP
  • Redshift Spectrum using Parquet

We performed benchmarks for simple and complex queries on one month’s worth of data. We tested how much time it took to perform the query, and how consistent the results were when running the same query multiple times. The data we used for the tests was already partitioned by date and hour. Properly partitioning the data improves performance significantly and reduces query times.

Simple query

First, we tested a simple query aggregating billing data across a month:

SELECT 
  user_id, 
  count(*) AS impressions, 
  SUM(billing)::decimal /1000000 AS billing 
FROM <table_name> 
WHERE 
  date >= '2017-08-01' AND 
  date <= '2017-08-31'  
GROUP BY 
  user_id;

We ran the same query seven times and measured the response times (red marking the longest time and green the shortest time):

Execution Time (seconds)
  Amazon Redshift Redshift Spectrum
CSV
Redshift Spectrum Parquet
Run #1 39.65 45.11 11.92
Run #2 15.26 43.13 12.05
Run #3 15.27 46.47 13.38
Run #4 21.22 51.02 12.74
Run #5 17.27 43.35 11.76
Run #6 16.67 44.23 13.67
Run #7 25.37 40.39 12.75
Average 21.53  44.82 12.61

For simple queries, Amazon Redshift performed better than Redshift Spectrum, as we thought, because the data is local to Amazon Redshift.

What was surprising was that using Parquet data format in Redshift Spectrum significantly beat ‘traditional’ Amazon Redshift performance. For our queries, using Parquet data format with Redshift Spectrum delivered an average 40% performance gain over traditional Amazon Redshift. Furthermore, Redshift Spectrum showed high consistency in execution time with a smaller difference between the slowest run and the fastest run.

Comparing the amount of data scanned when using CSV/GZIP and Parquet, the difference was also significant:

Data Scanned (GB)
CSV (Gzip) 135.49
Parquet 2.83

Because we pay only for the data scanned by Redshift Spectrum, the cost saving of using Parquet is evident and substantial.

Complex query

Next, we compared the same three configurations with a complex query.

Execution Time (seconds)
  Amazon Redshift Redshift Spectrum CSV Redshift Spectrum Parquet
Run #1 329.80 84.20 42.40
Run #2 167.60 65.30 35.10
Run #3 165.20 62.20 23.90
Run #4 273.90 74.90 55.90
Run #5 167.70 69.00 58.40
Average 220.84 71.12 43.14

This time, Redshift Spectrum using Parquet cut the average query time by 80% compared to traditional Amazon Redshift!

Bottom line: For complex queries, Redshift Spectrum provided a 67% performance gain over Amazon Redshift. Using the Parquet data format, Redshift Spectrum delivered an 80% performance improvement over Amazon Redshift. For us, this was substantial.

Optimizing the data structure for different workloads

Because the cost of S3 is relatively inexpensive and we pay only for the data scanned by each query, we believe that it makes sense to keep our data in different formats for different workloads and different analytics engines. It is important to note that we can have any number of tables pointing to the same data on S3. It all depends on how we partition the data and update the table partitions.

Data permutations

For example, we have a process that runs every minute and generates statistics for the last minute of data collected. With Amazon Redshift, this would be done by running the query on the table with something as follows:

SELECT 
  user, 
  COUNT(*) 
FROM 
  events_table 
WHERE 
  ts BETWEEN ‘2017-08-01 14:00:00’ AND ‘2017-08-01 14:00:59’ 
GROUP BY 
  user;

(Assuming ‘ts’ is your column storing the time stamp for each event.)

With Redshift Spectrum, we pay for the data scanned in each query. If the data is partitioned by the minute instead of the hour, a query looking at one minute would be 1/60th the cost. If we use a temporary table that points only to the data of the last minute, we save that unnecessary cost.

Creating Parquet data efficiently

On the average, we have 800 instances that process our traffic. Each instance sends events that are eventually loaded into Amazon Redshift. When we started three years ago, we would offload data from each server to S3 and then perform a periodic copy command from S3 to Amazon Redshift.

Recently, Amazon Kinesis Firehose added the capability to offload data directly to Amazon Redshift. While this is now a viable option, we kept the same collection process that worked flawlessly and efficiently for three years.

This changed, however, when we incorporated Redshift Spectrum. With Redshift Spectrum, we needed to find a way to:

  • Collect the event data from the instances.
  • Save the data in Parquet format.
  • Partition the data effectively.

To accomplish this, we save the data as CSV and then transform it to Parquet. The most effective method to generate the Parquet files is to:

  1. Send the data in one-minute intervals from the instances to Kinesis Firehose with an S3 temporary bucket as the destination.
  2. Aggregate hourly data and convert it to Parquet using AWS Lambda and AWS Glue.
  3. Add the Parquet data to S3 by updating the table partitions.

With this new process, we had to give more attention to validating the data before we sent it to Kinesis Firehose, because a single corrupted record in a partition fails queries on that partition.

Data validation

To store our click data in a table, we considered the following SQL create table command:

create external TABLE spectrum.blog_clicks (
    user_id varchar(50),
    campaign_id varchar(50),
    os varchar(50),
    ua varchar(255),
    ts bigint,
    billing float
)
partitioned by (date date, hour smallint)  
stored as parquet
location 's3://nuviad-temp/blog/clicks/';

The above statement defines a new external table (all Redshift Spectrum tables are external tables) with a few attributes. We stored ‘ts’ as a Unix time stamp and not as Timestamp, and billing data is stored as float and not decimal (more on that later). We also said that the data is partitioned by date and hour, and then stored as Parquet on S3.

First, we need to get the table definitions. This can be achieved by running the following query:

SELECT 
  * 
FROM 
  svv_external_columns 
WHERE 
  tablename = 'blog_clicks';

This query lists all the columns in the table with their respective definitions:

schemaname tablename columnname external_type columnnum part_key
spectrum blog_clicks user_id varchar(50) 1 0
spectrum blog_clicks campaign_id varchar(50) 2 0
spectrum blog_clicks os varchar(50) 3 0
spectrum blog_clicks ua varchar(255) 4 0
spectrum blog_clicks ts bigint 5 0
spectrum blog_clicks billing double 6 0
spectrum blog_clicks date date 7 1
spectrum blog_clicks hour smallint 8 2

Now we can use this data to create a validation schema for our data:

const rtb_request_schema = {
    "name": "clicks",
    "items": {
        "user_id": {
            "type": "string",
            "max_length": 100
        },
        "campaign_id": {
            "type": "string",
            "max_length": 50
        },
        "os": {
            "type": "string",
            "max_length": 50            
        },
        "ua": {
            "type": "string",
            "max_length": 255            
        },
        "ts": {
            "type": "integer",
            "min_value": 0,
            "max_value": 9999999999999
        },
        "billing": {
            "type": "float",
            "min_value": 0,
            "max_value": 9999999999999
        }
    }
};

Next, we create a function that uses this schema to validate data:

function valueIsValid(value, item_schema) {
    if (schema.type == 'string') {
        return (typeof value == 'string' && value.length <= schema.max_length);
    }
    else if (schema.type == 'integer') {
        return (typeof value == 'number' && value >= schema.min_value && value <= schema.max_value);
    }
    else if (schema.type == 'float' || schema.type == 'double') {
        return (typeof value == 'number' && value >= schema.min_value && value <= schema.max_value);
    }
    else if (schema.type == 'boolean') {
        return typeof value == 'boolean';
    }
    else if (schema.type == 'timestamp') {
        return (new Date(value)).getTime() > 0;
    }
    else {
        return true;
    }
}

Near real-time data loading with Kinesis Firehose

On Kinesis Firehose, we created a new delivery stream to handle the events as follows:

Delivery stream name: events
Source: Direct PUT
S3 bucket: nuviad-events
S3 prefix: rtb/
IAM role: firehose_delivery_role_1
Data transformation: Disabled
Source record backup: Disabled
S3 buffer size (MB): 100
S3 buffer interval (sec): 60
S3 Compression: GZIP
S3 Encryption: No Encryption
Status: ACTIVE
Error logging: Enabled

This delivery stream aggregates event data every minute, or up to 100 MB, and writes the data to an S3 bucket as a CSV/GZIP compressed file. Next, after we have the data validated, we can safely send it to our Kinesis Firehose API:

if (validated) {
    let itemString = item.join('|')+'\n'; //Sending csv delimited by pipe and adding new line

    let params = {
        DeliveryStreamName: 'events',
        Record: {
            Data: itemString
        }
    };

    firehose.putRecord(params, function(err, data) {
        if (err) {
            console.error(err, err.stack);        
        }
        else {
            // Continue to your next step 
        }
    });
}

Now, we have a single CSV file representing one minute of event data stored in S3. The files are named automatically by Kinesis Firehose by adding a UTC time prefix in the format YYYY/MM/DD/HH before writing objects to S3. Because we use the date and hour as partitions, we need to change the file naming and location to fit our Redshift Spectrum schema.

Automating data distribution using AWS Lambda

We created a simple Lambda function triggered by an S3 put event that copies the file to a different location (or locations), while renaming it to fit our data structure and processing flow. As mentioned before, the files generated by Kinesis Firehose are structured in a pre-defined hierarchy, such as:

S3://your-bucket/your-prefix/2017/08/01/20/events-4-2017-08-01-20-06-06-536f5c40-6893-4ee4-907d-81e4d3b09455.gz

All we need to do is parse the object name and restructure it as we see fit. In our case, we did the following (the event is an object received in the Lambda function with all the data about the object written to S3):

/*
	object key structure in the event object:
your-prefix/2017/08/01/20/event-4-2017-08-01-20-06-06-536f5c40-6893-4ee4-907d-81e4d3b09455.gz
	*/

let key_parts = event.Records[0].s3.object.key.split('/'); 

let event_type = key_parts[0];
let date = key_parts[1] + '-' + key_parts[2] + '-' + key_parts[3];
let hour = key_parts[4];
if (hour.indexOf('0') == 0) {
 		hour = parseInt(hour, 10) + '';
}
    
let parts1 = key_parts[5].split('-');
let minute = parts1[7];
if (minute.indexOf('0') == 0) {
        minute = parseInt(minute, 10) + '';
}

Now, we can redistribute the file to the two destinations we need—one for the minute processing task and the other for hourly aggregation:

    copyObjectToHourlyFolder(event, date, hour, minute)
        .then(copyObjectToMinuteFolder.bind(null, event, date, hour, minute))
        .then(addPartitionToSpectrum.bind(null, event, date, hour, minute))
        .then(deleteOldMinuteObjects.bind(null, event))
        .then(deleteStreamObject.bind(null, event))        
        .then(result => {
            callback(null, { message: 'done' });            
        })
        .catch(err => {
            console.error(err);
            callback(null, { message: err });            
        }); 

Kinesis Firehose stores the data in a temporary folder. We copy the object to another folder that holds the data for the last processed minute. This folder is connected to a small Redshift Spectrum table where the data is being processed without needing to scan a much larger dataset. We also copy the data to a folder that holds the data for the entire hour, to be later aggregated and converted to Parquet.

Because we partition the data by date and hour, we created a new partition on the Redshift Spectrum table if the processed minute is the first minute in the hour (that is, minute 0). We ran the following:

ALTER TABLE 
  spectrum.events 
ADD partition
  (date='2017-08-01', hour=0) 
  LOCATION 's3://nuviad-temp/events/2017-08-01/0/';

After the data is processed and added to the table, we delete the processed data from the temporary Kinesis Firehose storage and from the minute storage folder.

Migrating CSV to Parquet using AWS Glue and Amazon EMR

The simplest way we found to run an hourly job converting our CSV data to Parquet is using Lambda and AWS Glue (and thanks to the awesome AWS Big Data team for their help with this).

Creating AWS Glue jobs

What this simple AWS Glue script does:

  • Gets parameters for the job, date, and hour to be processed
  • Creates a Spark EMR context allowing us to run Spark code
  • Reads CSV data into a DataFrame
  • Writes the data as Parquet to the destination S3 bucket
  • Adds or modifies the Redshift Spectrum / Amazon Athena table partition for the table
import sys
import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
import boto3

## @params: [JOB_NAME]
args = getResolvedOptions(sys.argv, ['JOB_NAME','day_partition_key', 'hour_partition_key', 'day_partition_value', 'hour_partition_value' ])

#day_partition_key = "partition_0"
#hour_partition_key = "partition_1"
#day_partition_value = "2017-08-01"
#hour_partition_value = "0"

day_partition_key = args['day_partition_key']
hour_partition_key = args['hour_partition_key']
day_partition_value = args['day_partition_value']
hour_partition_value = args['hour_partition_value']

print("Running for " + day_partition_value + "/" + hour_partition_value)

sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)

df = spark.read.option("delimiter","|").csv("s3://nuviad-temp/events/"+day_partition_value+"/"+hour_partition_value)
df.registerTempTable("data")

df1 = spark.sql("select _c0 as user_id, _c1 as campaign_id, _c2 as os, _c3 as ua, cast(_c4 as bigint) as ts, cast(_c5 as double) as billing from data")

df1.repartition(1).write.mode("overwrite").parquet("s3://nuviad-temp/parquet/"+day_partition_value+"/hour="+hour_partition_value)

client = boto3.client('athena', region_name='us-east-1')

response = client.start_query_execution(
    QueryString='alter table parquet_events add if not exists partition(' + day_partition_key + '=\'' + day_partition_value + '\',' + hour_partition_key + '=' + hour_partition_value + ')  location \'s3://nuviad-temp/parquet/' + day_partition_value + '/hour=' + hour_partition_value + '\'' ,
    QueryExecutionContext={
        'Database': 'spectrumdb'
    },
    ResultConfiguration={
        'OutputLocation': 's3://nuviad-temp/convertresults'
    }
)

response = client.start_query_execution(
    QueryString='alter table parquet_events partition(' + day_partition_key + '=\'' + day_partition_value + '\',' + hour_partition_key + '=' + hour_partition_value + ') set location \'s3://nuviad-temp/parquet/' + day_partition_value + '/hour=' + hour_partition_value + '\'' ,
    QueryExecutionContext={
        'Database': 'spectrumdb'
    },
    ResultConfiguration={
        'OutputLocation': 's3://nuviad-temp/convertresults'
    }
)

job.commit()

Note: Because Redshift Spectrum and Athena both use the AWS Glue Data Catalog, we could use the Athena client to add the partition to the table.

Here are a few words about float, decimal, and double. Using decimal proved to be more challenging than we expected, as it seems that Redshift Spectrum and Spark use them differently. Whenever we used decimal in Redshift Spectrum and in Spark, we kept getting errors, such as:

S3 Query Exception (Fetch). Task failed due to an internal error. File 'https://s3-external-1.amazonaws.com/nuviad-temp/events/2017-08-01/hour=2/part-00017-48ae5b6b-906e-4875-8cde-bc36c0c6d0ca.c000.snappy.parquet has an incompatible Parquet schema for column 's3://nuviad-events/events.lat'. Column type: DECIMAL(18, 8), Parquet schema:\noptional float lat [i:4 d:1 r:0]\n (https://s3-external-1.amazonaws.com/nuviad-temp/events/2017-08-01/hour=2/part-00017-48ae5b6b-906e-4875-8cde-bc36c0c6d0ca.c000.snappy.parq

We had to experiment with a few floating-point formats until we found that the only combination that worked was to define the column as double in the Spark code and float in Spectrum. This is the reason you see billing defined as float in Spectrum and double in the Spark code.

Creating a Lambda function to trigger conversion

Next, we created a simple Lambda function to trigger the AWS Glue script hourly using a simple Python code:

import boto3
import json
from datetime import datetime, timedelta
 
client = boto3.client('glue')
 
def lambda_handler(event, context):
    last_hour_date_time = datetime.now() - timedelta(hours = 1)
    day_partition_value = last_hour_date_time.strftime("%Y-%m-%d") 
    hour_partition_value = last_hour_date_time.strftime("%-H") 
    response = client.start_job_run(
    JobName='convertEventsParquetHourly',
    Arguments={
         '--day_partition_key': 'date',
         '--hour_partition_key': 'hour',
         '--day_partition_value': day_partition_value,
         '--hour_partition_value': hour_partition_value
         }
    )

Using Amazon CloudWatch Events, we trigger this function hourly. This function triggers an AWS Glue job named ‘convertEventsParquetHourly’ and runs it for the previous hour, passing job names and values of the partitions to process to AWS Glue.

Redshift Spectrum and Node.js

Our development stack is based on Node.js, which is well-suited for high-speed, light servers that need to process a huge number of transactions. However, a few limitations of the Node.js environment required us to create workarounds and use other tools to complete the process.

Node.js and Parquet

The lack of Parquet modules for Node.js required us to implement an AWS Glue/Amazon EMR process to effectively migrate data from CSV to Parquet. We would rather save directly to Parquet, but we couldn’t find an effective way to do it.

One interesting project in the works is the development of a Parquet NPM by Marc Vertes called node-parquet (https://www.npmjs.com/package/node-parquet). It is not in a production state yet, but we think it would be well worth following the progress of this package.

Timestamp data type

According to the Parquet documentation, Timestamp data are stored in Parquet as 64-bit integers. However, JavaScript does not support 64-bit integers, because the native number type is a 64-bit double, giving only 53 bits of integer range.

The result is that you cannot store Timestamp correctly in Parquet using Node.js. The solution is to store Timestamp as string and cast the type to Timestamp in the query. Using this method, we did not witness any performance degradation whatsoever.

Lessons learned

You can benefit from our trial-and-error experience.

Lesson #1: Data validation is critical

As mentioned earlier, a single corrupt entry in a partition can fail queries running against this partition, especially when using Parquet, which is harder to edit than a simple CSV file. Make sure that you validate your data before scanning it with Redshift Spectrum.

Lesson #2: Structure and partition data effectively

One of the biggest benefits of using Redshift Spectrum (or Athena for that matter) is that you don’t need to keep nodes up and running all the time. You pay only for the queries you perform and only for the data scanned per query.

Keeping different permutations of your data for different queries makes a lot of sense in this case. For example, you can partition your data by date and hour to run time-based queries, and also have another set partitioned by user_id and date to run user-based queries. This results in faster and more efficient performance of your data warehouse.

Storing data in the right format

Use Parquet whenever you can. The benefits of Parquet are substantial. Faster performance, less data to scan, and much more efficient columnar format. However, it is not supported out-of-the-box by Kinesis Firehose, so you need to implement your own ETL. AWS Glue is a great option.

Creating small tables for frequent tasks

When we started using Redshift Spectrum, we saw our Amazon Redshift costs jump by hundreds of dollars per day. Then we realized that we were unnecessarily scanning a full day’s worth of data every minute. Take advantage of the ability to define multiple tables on the same S3 bucket or folder, and create temporary and small tables for frequent queries.

Lesson #3: Combine Athena and Redshift Spectrum for optimal performance

Moving to Redshift Spectrum also allowed us to take advantage of Athena as both use the AWS Glue Data Catalog. Run fast and simple queries using Athena while taking advantage of the advanced Amazon Redshift query engine for complex queries using Redshift Spectrum.

Redshift Spectrum excels when running complex queries. It can push many compute-intensive tasks, such as predicate filtering and aggregation, down to the Redshift Spectrum layer, so that queries use much less of your cluster’s processing capacity.

Lesson #4: Sort your Parquet data within the partition

We achieved another performance improvement by sorting data within the partition using sortWithinPartitions(sort_field). For example:

df.repartition(1).sortWithinPartitions("campaign_id")…

Conclusion

We were extremely pleased with using Amazon Redshift as our core data warehouse for over three years. But as our client base and volume of data grew substantially, we extended Amazon Redshift to take advantage of scalability, performance, and cost with Redshift Spectrum.

Redshift Spectrum lets us scale to virtually unlimited storage, scale compute transparently, and deliver super-fast results for our users. With Redshift Spectrum, we store data where we want at the cost we want, and have the data available for analytics when our users need it with the performance they expect.


About the Author

With 7 years of experience in the AdTech industry and 15 years in leading technology companies, Rafi Ton is the founder and CEO of NUVIAD. He enjoys exploring new technologies and putting them to use in cutting edge products and services, in the real world generating real money. Being an experienced entrepreneur, Rafi believes in practical-programming and fast adaptation of new technologies to achieve a significant market advantage.

 

 

Eevee mugshot set for Doom

Post Syndicated from Eevee original https://eev.ee/release/2017/11/23/eevee-mugshot-set-for-doom/

Screenshot of Industrial Zone from Doom II, with an Eevee face replacing the usual Doom marine in the status bar

A full replacement of Doomguy’s vast array of 42 expressions.

You can get it yourself if you want to play Doom as me, for some reason? It does nothing but replace a few sprites, so it works with any Doom flavor (including vanilla) on 1, 2, or Final. Just run Doom with -file eeveemug.wad. With GZDoom, you can load it automatically.


I don’t entirely know why I did this. I drew the first one on a whim, then realized there was nothing really stopping me from making a full set, so I spent a day doing that.

The funny thing is that I usually play Doom with ZDoom’s “alternate” HUD. It’s a full-screen overlay rather than a huge bar, and — crucially — it does not show the mugshot. It can’t even be configured to show the mugshot. As far as I’m aware, it can’t even be modded to show the mugshot. So I have to play with the OG status bar if I want to actually use the thing I made.

Preview of the Eevee mugshot sprites arranged in a grid, where the Eevee becomes more beaten up in each subsequent column

I’m pretty happy with the results overall! I think I did a decent job emulating the Doom “surreal grit” style. I did the shading with Aseprite‘s shading mode — instead of laying down a solid color, it shifts pixels along a ramp of colors you select every time you draw over them. Doom’s palette has a lot of browns, so I made a ramp out of all of them and kept going over furry areas, nudging pixels into being lighter or darker, until I liked the texture. It was a lot like making a texture in a sketch with a lot of scratchy pencil strokes.

I also gleaned some interesting things about smoothness and how the eye interprets contours? I tried to explain this on Twitter and had a hell of a time putting it into words, but the short version is that it’s amazing to see the difference a single misplaced pixel can make, especially as you slide that pixel between dark and light.


Doom's palette of 256 colors, many of which are very long gradients of reds and browns

Speaking of which, Doom’s palette is incredibly weird to work with. Thank goodness Eevees are brown! The game does have to draw arbitrary levels of darkness all with the same palette, which partly explains the number of dark colors and gradients — but I believe a number of the colors are exact duplicates, so close they might as well be duplicates, or completely unused in stock Doom assets. I guess they had no reason to optimize for people trying to add arbitrary art to the game 25 years later, though. (And nowadays, GZDoom includes a truecolor software renderer, so the palette is becoming less and less important.)

I originally wanted the god mode sprite to be a Sylveon, but Sylveon is made of pink and azure and blurple, and I don’t think I could’ve pulled it off with this set of colors. I even struggled with the color of the mane a bit — I usually color it with pretty pale colors, but Doom only has a couple of those, and they’re very saturated. I ended up using a lot more dark yellows than I would normally, and thankfully it worked out pretty well.

The most significant change I made between the original sprite and the final set was the eye color:

A comparison between an original Doom mugshot sprite, the first sprite I drew, and how it ended up

(This is STFST20, a frame from the default three-frame “glacing around” animation that plays when the player has between 40 and 59 health. Doom Wiki has a whole article on the mugshot if you’re interested.)

The blue eyes in my original just do not work at all. The Doom palette doesn’t have a lot of subtle colors, and its blues in particular are incredibly bad. In the end, I made the eyes basically black, though with a couple pixels of very dark blue in them.

After I decided to make the full set, I started by making a neutral and completely healthy front pose, then derived the others from that (with a very complicated system of layers). You can see some of the side effects of that here: the face doesn’t actually turn when glancing around, because hoo boy that would’ve been a lot of work, and so the cheek fluff is visible on both sides.

I also notice that there are two columns of identical pixels in each eye! I fixed that in the glance to the right, but must’ve forgotten about it here. Oh, well; I didn’t even notice until I zoomed in just now.

A general comparison between the Doom mugshots and my Eevee ones, showing each pose in its healthy state plus the neutral pose in every state of deterioration

The original sprites might not be quite aligned correctly in the above image. The available space in the status bar is 35×31, of which a couple pixels go to an inset border, leaving 33×30. I drew all of my sprites at that size, but the originals are all cropped and have varying offsets (part of the Doom sprite format). I extremely can’t be assed to check all of those offsets for over a dozen sprites, so I just told ImageMagick to center them. (I only notice right now that some of the original sprites are even a full 31 pixels tall and draw over the top border that I was so careful to stay out of!)

Anyway, this is a representative sample of the Doom mugshot poses.

The top row shows all eight frames at full health. The first three are the “idle” state, drawn when nothing else is going on; the sprite usually faces forwards, but glances around every so often at random. The forward-facing sprite is the one I finalized first.

I tried to take a lot of cues from the original sprite, seeing as I wanted to match the style. I’d never tried drawing a sprite with a large palette and a small resolution before, and the first thing that struck me was Doomguy’s lips — the upper lip, lips themselves, and shadow under the lower lip are all created with only one row of pixels each. I thought that was amazing. Now I even kinda wish I’d exaggerated that effect a bit more, but I was wary of going too dark when there’s a shadow only a couple pixels away. I suppose Doomguy has the advantage of having, ah, a chin.

I did much the same for the eyebrows, which was especially necessary because Doomguy has more of a forehead than my Eevee does. I probably could’ve exaggerated those a bit more, as well! Still, I love how they came out — especially in the simple looking-around frames, where even a two-pixel eyebrow raise is almost comically smug.

The fourth frame is a wild-ass grin (even named STFEVL0), which shows for a short time after picking up a new weapon. Come to think of it, that’s a pretty rare occurrence when playing straight through one of the Doom games; you keep your weapons between levels.

The fifth through seventh are also a set. If the player takes damage, the status bar will briefly show one of these frames to indicate where the damage is coming from. You may notice that where Doomguy bravely faces the source of the pain, I drew myself wincing and recoiling away from it.

The middle frame of that set also appears while the player is firing continuously (regardless of damage), so I couldn’t really make it match the left and right ones. I like the result anyway. It was also great fun figuring out the expressions with the mouth — that’s another place where individual pixels make a huge difference.

Finally, the eighth column is the legendary “ouch” face, which appears when the player takes more than 20 damage at once. It may look completely alien to you, because vanilla Doom has a bug that only shows this face when the player gains 20 or more health while taking damage. This is vanishingly rare (though possible!), so the frame virtually never appears in vanilla Doom. Lots of source ports have fixed this bug, making the ouch face it a bit better known, but I usually play without the mugshot visible so it still looks super weird to me. I think my own spin on it is a bit less, ah, body horror?

The second row shows deterioration. It is pretty weird drawing yourself getting beaten up.

A lot of Doomguy’s deterioration is in the form of blood dripping from under his hair, which I didn’t think would translate terribly well to a character without hair. Instead, I went a little cartoony with it, adding bandages here and there. I had a little bit of a hard time with the bloodshot eyes at this resolution, which I realize as I type it is a very poor excuse when I had eyes three times bigger than Doomguy’s. I do love the drooping ears, with the possible exception of the fifth state, which I’m not sure is how that would actually look…? Oh well. I also like the bow becoming gradually unravelled, eventually falling off entirely when you die.

Oh, yes, the sixth frame there (before the gap) is actually for a dead player. Doomguy’s bleeding becomes markedly more extreme here, but again that didn’t really work for me, so I went a little sillier with it. A little. It’s still pretty weird drawing yourself dead.

That leaves only god mode, which is incredible. I love that glow. I love the faux whisker shapes it makes. I love how it fades into the background. I love that 100% pure “oh this is pretty good” smile. It all makes me want to just play Doom in god mode forever.

Now that I’ve looked closely at these sprites again, I spy a good half dozen little inconsistencies and nitpicks, which I’m going to refrain from spelling out. I did do this in only a day, and I think it came out pretty dang well considering.

Maybe I’ll try something else like this in the future. Not quite sure what, though; there aren’t many small and self-contained sets of sprites like this in Doom. Monsters are several times bigger and have a zillion different angles. Maybe some pickups, which only have one frame?


Hmm. Parting thought: I’m not quite sure where I should host this sort of one-off thing. It arguably belongs on Itch, but seems really out of place alongside entire released games. It also arguably belongs on the idgames archive, but I’m hesitant to put it there because it’s such an obscure thing of little interest to a general audience. At the moment it’s just a file I’ve uploaded to wherever on my own space, but I now have three little Doom experiments with no real permanent home.

How to Patch, Inspect, and Protect Microsoft Windows Workloads on AWS—Part 2

Post Syndicated from Koen van Blijderveen original https://aws.amazon.com/blogs/security/how-to-patch-inspect-and-protect-microsoft-windows-workloads-on-aws-part-2/

Yesterday in Part 1 of this blog post, I showed you how to:

  1. Launch an Amazon EC2 instance with an AWS Identity and Access Management (IAM) role, an Amazon Elastic Block Store (Amazon EBS) volume, and tags that Amazon EC2 Systems Manager (Systems Manager) and Amazon Inspector use.
  2. Configure Systems Manager to install the Amazon Inspector agent and patch your EC2 instances.

Today in Steps 3 and 4, I show you how to:

  1. Take Amazon EBS snapshots using Amazon EBS Snapshot Scheduler to automate snapshots based on instance tags.
  2. Use Amazon Inspector to check if your EC2 instances running Microsoft Windows contain any common vulnerabilities and exposures (CVEs).

To catch up on Steps 1 and 2, see yesterday’s blog post.

Step 3: Take EBS snapshots using EBS Snapshot Scheduler

In this section, I show you how to use EBS Snapshot Scheduler to take snapshots of your instances at specific intervals. To do this, I will show you how to:

  • Determine the schedule for EBS Snapshot Scheduler by providing you with best practices.
  • Deploy EBS Snapshot Scheduler by using AWS CloudFormation.
  • Tag your EC2 instances so that EBS Snapshot Scheduler backs up your instances when you want them backed up.

In addition to making sure your EC2 instances have all the available operating system patches applied on a regular schedule, you should take snapshots of the EBS storage volumes attached to your EC2 instances. Taking regular snapshots allows you to restore your data to a previous state quickly and cost effectively. With Amazon EBS snapshots, you pay only for the actual data you store, and snapshots save only the data that has changed since the previous snapshot, which minimizes your cost. You will use EBS Snapshot Scheduler to make regular snapshots of your EC2 instance. EBS Snapshot Scheduler takes advantage of other AWS services including CloudFormation, Amazon DynamoDB, and AWS Lambda to make backing up your EBS volumes simple.

Determine the schedule

As a best practice, you should back up your data frequently during the hours when your data changes the most. This reduces the amount of data you lose if you have to restore from a snapshot. For the purposes of this blog post, the data for my instances changes the most between the business hours of 9:00 A.M. to 5:00 P.M. Pacific Time. During these hours, I will make snapshots hourly to minimize data loss.

In addition to backing up frequently, another best practice is to establish a strategy for retention. This will vary based on how you need to use the snapshots. If you have compliance requirements to be able to restore for auditing, your needs may be different than if you are able to detect data corruption within three hours and simply need to restore to something that limits data loss to five hours. EBS Snapshot Scheduler enables you to specify the retention period for your snapshots. For this post, I only need to keep snapshots for recent business days. To account for weekends, I will set my retention period to three days, which is down from the default of 15 days when deploying EBS Snapshot Scheduler.

Deploy EBS Snapshot Scheduler

In Step 1 of Part 1 of this post, I showed how to configure an EC2 for Windows Server 2012 R2 instance with an EBS volume. You will use EBS Snapshot Scheduler to take eight snapshots each weekday of your EC2 instance’s EBS volumes:

  1. Navigate to the EBS Snapshot Scheduler deployment page and choose Launch Solution. This takes you to the CloudFormation console in your account. The Specify an Amazon S3 template URL option is already selected and prefilled. Choose Next on the Select Template page.
  2. On the Specify Details page, retain all default parameters except for AutoSnapshotDeletion. Set AutoSnapshotDeletion to Yes to ensure that old snapshots are periodically deleted. The default retention period is 15 days (you will specify a shorter value on your instance in the next subsection).
  3. Choose Next twice to move to the Review step, and start deployment by choosing the I acknowledge that AWS CloudFormation might create IAM resources check box and then choosing Create.

Tag your EC2 instances

EBS Snapshot Scheduler takes a few minutes to deploy. While waiting for its deployment, you can start to tag your instance to define its schedule. EBS Snapshot Scheduler reads tag values and looks for four possible custom parameters in the following order:

  • <snapshot time> – Time in 24-hour format with no colon.
  • <retention days> – The number of days (a positive integer) to retain the snapshot before deletion, if set to automatically delete snapshots.
  • <time zone> – The time zone of the times specified in <snapshot time>.
  • <active day(s)>all, weekdays, or mon, tue, wed, thu, fri, sat, and/or sun.

Because you want hourly backups on weekdays between 9:00 A.M. and 5:00 P.M. Pacific Time, you need to configure eight tags—one for each hour of the day. You will add the eight tags shown in the following table to your EC2 instance.

Tag Value
scheduler:ebs-snapshot:0900 0900;3;utc;weekdays
scheduler:ebs-snapshot:1000 1000;3;utc;weekdays
scheduler:ebs-snapshot:1100 1100;3;utc;weekdays
scheduler:ebs-snapshot:1200 1200;3;utc;weekdays
scheduler:ebs-snapshot:1300 1300;3;utc;weekdays
scheduler:ebs-snapshot:1400 1400;3;utc;weekdays
scheduler:ebs-snapshot:1500 1500;3;utc;weekdays
scheduler:ebs-snapshot:1600 1600;3;utc;weekdays

Next, you will add these tags to your instance. If you want to tag multiple instances at once, you can use Tag Editor instead. To add the tags in the preceding table to your EC2 instance:

  1. Navigate to your EC2 instance in the EC2 console and choose Tags in the navigation pane.
  2. Choose Add/Edit Tags and then choose Create Tag to add all the tags specified in the preceding table.
  3. Confirm you have added the tags by choosing Save. After adding these tags, navigate to your EC2 instance in the EC2 console. Your EC2 instance should look similar to the following screenshot.
    Screenshot of how your EC2 instance should look in the console
  4. After waiting a couple of hours, you can see snapshots beginning to populate on the Snapshots page of the EC2 console.Screenshot of snapshots beginning to populate on the Snapshots page of the EC2 console
  5. To check if EBS Snapshot Scheduler is active, you can check the CloudWatch rule that runs the Lambda function. If the clock icon shown in the following screenshot is green, the scheduler is active. If the clock icon is gray, the rule is disabled and does not run. You can enable or disable the rule by selecting it, choosing Actions, and choosing Enable or Disable. This also allows you to temporarily disable EBS Snapshot Scheduler.Screenshot of checking to see if EBS Snapshot Scheduler is active
  1. You can also monitor when EBS Snapshot Scheduler has run by choosing the name of the CloudWatch rule as shown in the previous screenshot and choosing Show metrics for the rule.Screenshot of monitoring when EBS Snapshot Scheduler has run by choosing the name of the CloudWatch rule

If you want to restore and attach an EBS volume, see Restoring an Amazon EBS Volume from a Snapshot and Attaching an Amazon EBS Volume to an Instance.

Step 4: Use Amazon Inspector

In this section, I show you how to you use Amazon Inspector to scan your EC2 instance for common vulnerabilities and exposures (CVEs) and set up Amazon SNS notifications. To do this I will show you how to:

  • Install the Amazon Inspector agent by using EC2 Run Command.
  • Set up notifications using Amazon SNS to notify you of any findings.
  • Define an Amazon Inspector target and template to define what assessment to perform on your EC2 instance.
  • Schedule Amazon Inspector assessment runs to assess your EC2 instance on a regular interval.

Amazon Inspector can help you scan your EC2 instance using prebuilt rules packages, which are built and maintained by AWS. These prebuilt rules packages tell Amazon Inspector what to scan for on the EC2 instances you select. Amazon Inspector provides the following prebuilt packages for Microsoft Windows Server 2012 R2:

  • Common Vulnerabilities and Exposures
  • Center for Internet Security Benchmarks
  • Runtime Behavior Analysis

In this post, I’m focused on how to make sure you keep your EC2 instances patched, backed up, and inspected for common vulnerabilities and exposures (CVEs). As a result, I will focus on how to use the CVE rules package and use your instance tags to identify the instances on which to run the CVE rules. If your EC2 instance is fully patched using Systems Manager, as described earlier, you should not have any findings with the CVE rules package. Regardless, as a best practice I recommend that you use Amazon Inspector as an additional layer for identifying any unexpected failures. This involves using Amazon CloudWatch to set up weekly Amazon Inspector scans, and configuring Amazon Inspector to notify you of any findings through SNS topics. By acting on the notifications you receive, you can respond quickly to any CVEs on any of your EC2 instances to help ensure that malware using known CVEs does not affect your EC2 instances. In a previous blog post, Eric Fitzgerald showed how to remediate Amazon Inspector security findings automatically.

Install the Amazon Inspector agent

To install the Amazon Inspector agent, you will use EC2 Run Command, which allows you to run any command on any of your EC2 instances that have the Systems Manager agent with an attached IAM role that allows access to Systems Manager.

  1. Choose Run Command under Systems Manager Services in the navigation pane of the EC2 console. Then choose Run a command.
    Screenshot of choosing "Run a command"
  2. To install the Amazon Inspector agent, you will use an AWS managed and provided command document that downloads and installs the agent for you on the selected EC2 instance. Choose AmazonInspector-ManageAWSAgent. To choose the target EC2 instance where this command will be run, use the tag you previously assigned to your EC2 instance, Patch Group, with a value of Windows Servers. For this example, set the concurrent installations to 1 and tell Systems Manager to stop after 5 errors.
    Screenshot of installing the Amazon Inspector agent
  3. Retain the default values for all other settings on the Run a command page and choose Run. Back on the Run Command page, you can see if the command that installed the Amazon Inspector agent executed successfully on all selected EC2 instances.
    Screenshot showing that the command that installed the Amazon Inspector agent executed successfully on all selected EC2 instances

Set up notifications using Amazon SNS

Now that you have installed the Amazon Inspector agent, you will set up an SNS topic that will notify you of any findings after an Amazon Inspector run.

To set up an SNS topic:

  1. In the AWS Management Console, choose Simple Notification Service under Messaging in the Services menu.
  2. Choose Create topic, name your topic (only alphanumeric characters, hyphens, and underscores are allowed) and give it a display name to ensure you know what this topic does (I’ve named mine Inspector). Choose Create topic.
    "Create new topic" page
  3. To allow Amazon Inspector to publish messages to your new topic, choose Other topic actions and choose Edit topic policy.
  4. For Allow these users to publish messages to this topic and Allow these users to subscribe to this topic, choose Only these AWS users. Type the following ARN for the US East (N. Virginia) Region in which you are deploying the solution in this post: arn:aws:iam::316112463485:root. This is the ARN of Amazon Inspector itself. For the ARNs of Amazon Inspector in other AWS Regions, see Setting Up an SNS Topic for Amazon Inspector Notifications (Console). Amazon Resource Names (ARNs) uniquely identify AWS resources across all of AWS.
    Screenshot of editing the topic policy
  5. To receive notifications from Amazon Inspector, subscribe to your new topic by choosing Create subscription and adding your email address. After confirming your subscription by clicking the link in the email, the topic should display your email address as a subscriber. Later, you will configure the Amazon Inspector template to publish to this topic.
    Screenshot of subscribing to the new topic

Define an Amazon Inspector target and template

Now that you have set up the notification topic by which Amazon Inspector can notify you of findings, you can create an Amazon Inspector target and template. A target defines which EC2 instances are in scope for Amazon Inspector. A template defines which packages to run, for how long, and on which target.

To create an Amazon Inspector target:

  1. Navigate to the Amazon Inspector console and choose Get started. At the time of writing this blog post, Amazon Inspector is available in the US East (N. Virginia), US West (N. California), US West (Oregon), EU (Ireland), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Sydney), and Asia Pacific (Tokyo) Regions.
  2. For Amazon Inspector to be able to collect the necessary data from your EC2 instance, you must create an IAM service role for Amazon Inspector. Amazon Inspector can create this role for you if you choose Choose or create role and confirm the role creation by choosing Allow.
    Screenshot of creating an IAM service role for Amazon Inspector
  3. Amazon Inspector also asks you to tag your EC2 instance and install the Amazon Inspector agent. You already performed these steps in Part 1 of this post, so you can proceed by choosing Next. To define the Amazon Inspector target, choose the previously used Patch Group tag with a Value of Windows Servers. This is the same tag that you used to define the targets for patching. Then choose Next.
    Screenshot of defining the Amazon Inspector target
  4. Now, define your Amazon Inspector template, and choose a name and the package you want to run. For this post, use the Common Vulnerabilities and Exposures package and choose the default duration of 1 hour. As you can see, the package has a version number, so always select the latest version of the rules package if multiple versions are available.
    Screenshot of defining an assessment template
  5. Configure Amazon Inspector to publish to your SNS topic when findings are reported. You can also choose to receive a notification of a started run, a finished run, or changes in the state of a run. For this blog post, you want to receive notifications if there are any findings. To start, choose Assessment Templates from the Amazon Inspector console and choose your newly created Amazon Inspector assessment template. Choose the icon below SNS topics (see the following screenshot).
    Screenshot of choosing an assessment template
  6. A pop-up appears in which you can choose the previously created topic and the events about which you want SNS to notify you (choose Finding reported).
    Screenshot of choosing the previously created topic and the events about which you want SNS to notify you

Schedule Amazon Inspector assessment runs

The last step in using Amazon Inspector to assess for CVEs is to schedule the Amazon Inspector template to run using Amazon CloudWatch Events. This will make sure that Amazon Inspector assesses your EC2 instance on a regular basis. To do this, you need the Amazon Inspector template ARN, which you can find under Assessment templates in the Amazon Inspector console. CloudWatch Events can run your Amazon Inspector assessment at an interval you define using a Cron-based schedule. Cron is a well-known scheduling agent that is widely used on UNIX-like operating systems and uses the following syntax for CloudWatch Events.

Image of Cron schedule

All scheduled events use a UTC time zone, and the minimum precision for schedules is one minute. For more information about scheduling CloudWatch Events, see Schedule Expressions for Rules.

To create the CloudWatch Events rule:

  1. Navigate to the CloudWatch console, choose Events, and choose Create rule.
    Screenshot of starting to create a rule in the CloudWatch Events console
  2. On the next page, specify if you want to invoke your rule based on an event pattern or a schedule. For this blog post, you will select a schedule based on a Cron expression.
  3. You can schedule the Amazon Inspector assessment any time you want using the Cron expression, or you can use the Cron expression I used in the following screenshot, which will run the Amazon Inspector assessment every Sunday at 10:00 P.M. GMT.
    Screenshot of scheduling an Amazon Inspector assessment with a Cron expression
  4. Choose Add target and choose Inspector assessment template from the drop-down menu. Paste the ARN of the Amazon Inspector template you previously created in the Amazon Inspector console in the Assessment template box and choose Create a new role for this specific resource. This new role is necessary so that CloudWatch Events has the necessary permissions to start the Amazon Inspector assessment. CloudWatch Events will automatically create the new role and grant the minimum set of permissions needed to run the Amazon Inspector assessment. To proceed, choose Configure details.
    Screenshot of adding a target
  5. Next, give your rule a name and a description. I suggest using a name that describes what the rule does, as shown in the following screenshot.
  6. Finish the wizard by choosing Create rule. The rule should appear in the Events – Rules section of the CloudWatch console.
    Screenshot of completing the creation of the rule
  7. To confirm your CloudWatch Events rule works, wait for the next time your CloudWatch Events rule is scheduled to run. For testing purposes, you can choose your CloudWatch Events rule and choose Edit to change the schedule to run it sooner than scheduled.
    Screenshot of confirming the CloudWatch Events rule works
  8. Now navigate to the Amazon Inspector console to confirm the launch of your first assessment run. The Start time column shows you the time each assessment started and the Status column the status of your assessment. In the following screenshot, you can see Amazon Inspector is busy Collecting data from the selected assessment targets.
    Screenshot of confirming the launch of the first assessment run

You have concluded the last step of this blog post by setting up a regular scan of your EC2 instance with Amazon Inspector and a notification that will let you know if your EC2 instance is vulnerable to any known CVEs. In a previous Security Blog post, Eric Fitzgerald explained How to Remediate Amazon Inspector Security Findings Automatically. Although that blog post is for Linux-based EC2 instances, the post shows that you can learn about Amazon Inspector findings in other ways than email alerts.

Conclusion

In this two-part blog post, I showed how to make sure you keep your EC2 instances up to date with patching, how to back up your instances with snapshots, and how to monitor your instances for CVEs. Collectively these measures help to protect your instances against common attack vectors that attempt to exploit known vulnerabilities. In Part 1, I showed how to configure your EC2 instances to make it easy to use Systems Manager, EBS Snapshot Scheduler, and Amazon Inspector. I also showed how to use Systems Manager to schedule automatic patches to keep your instances current in a timely fashion. In Part 2, I showed you how to take regular snapshots of your data by using EBS Snapshot Scheduler and how to use Amazon Inspector to check if your EC2 instances running Microsoft Windows contain any common vulnerabilities and exposures (CVEs).

If you have comments about today’s or yesterday’s post, submit them in the “Comments” section below. If you have questions about or issues implementing any part of this solution, start a new thread on the Amazon EC2 forum or the Amazon Inspector forum, or contact AWS Support.

– Koen

Amazon QuickSight Update – Geospatial Visualization, Private VPC Access, and More

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/amazon-quicksight-update-geospatial-visualization-private-vpc-access-and-more/

We don’t often recognize or celebrate anniversaries at AWS. With nearly 100 services on our list, we’d be eating cake and drinking champagne several times a week. While that might sound like fun, we’d rather spend our working hours listening to customers and innovating. With that said, Amazon QuickSight has now been generally available for a little over a year and I would like to give you a quick update!

QuickSight in Action
Today, tens of thousands of customers (from startups to enterprises, in industries as varied as transportation, legal, mining, and healthcare) are using QuickSight to analyze and report on their business data.

Here are a couple of examples:

Gemini provides legal evidence procurement for California attorneys who represent injured workers. They have gone from creating custom reports and running one-off queries to creating and sharing dynamic QuickSight dashboards with drill-downs and filtering. QuickSight is used to track sales pipeline, measure order throughput, and to locate bottlenecks in the order processing pipeline.

Jivochat provides a real-time messaging platform to connect visitors to website owners. QuickSight lets them create and share interactive dashboards while also providing access to the underlying datasets. This has allowed them to move beyond the sharing of static spreadsheets, ensuring that everyone is looking at the same and is empowered to make timely decisions based on current data.

Transfix is a tech-powered freight marketplace that matches loads and increases visibility into logistics for Fortune 500 shippers in retail, food and beverage, manufacturing, and other industries. QuickSight has made analytics accessible to both BI engineers and non-technical business users. They scrutinize key business and operational metrics including shipping routes, carrier efficient, and process automation.

Looking Back / Looking Ahead
The feedback on QuickSight has been incredibly helpful. Customers tell us that their employees are using QuickSight to connect to their data, perform analytics, and make high-velocity, data-driven decisions, all without setting up or running their own BI infrastructure. We love all of the feedback that we get, and use it to drive our roadmap, leading to the introduction of over 40 new features in just a year. Here’s a summary:

Looking forward, we are watching an interesting trend develop within our customer base. As these customers take a close look at how they analyze and report on data, they are realizing that a serverless approach offers some tangible benefits. They use Amazon Simple Storage Service (S3) as a data lake and query it using a combination of QuickSight and Amazon Athena, giving them agility and flexibility without static infrastructure. They also make great use of QuickSight’s dashboards feature, monitoring business results and operational metrics, then sharing their insights with hundreds of users. You can read Building a Serverless Analytics Solution for Cleaner Cities and review Serverless Big Data Analytics using Amazon Athena and Amazon QuickSight if you are interested in this approach.

New Features and Enhancements
We’re still doing our best to listen and to learn, and to make sure that QuickSight continues to meet your needs. I’m happy to announce that we are making seven big additions today:

Geospatial Visualization – You can now create geospatial visuals on geographical data sets.

Private VPC Access – You can now sign up to access a preview of a new feature that allows you to securely connect to data within VPCs or on-premises, without the need for public endpoints.

Flat Table Support – In addition to pivot tables, you can now use flat tables for tabular reporting. To learn more, read about Using Tabular Reports.

Calculated SPICE Fields – You can now perform run-time calculations on SPICE data as part of your analysis. Read Adding a Calculated Field to an Analysis for more information.

Wide Table Support – You can now use tables with up to 1000 columns.

Other Buckets – You can summarize the long tail of high-cardinality data into buckets, as described in Working with Visual Types in Amazon QuickSight.

HIPAA Compliance – You can now run HIPAA-compliant workloads on QuickSight.

Geospatial Visualization
Everyone seems to want this feature! You can now take data that contains a geographic identifier (country, city, state, or zip code) and create beautiful visualizations with just a few clicks. QuickSight will geocode the identifier that you supply, and can also accept lat/long map coordinates. You can use this feature to visualize sales by state, map stores to shipping destinations, and so forth. Here’s a sample visualization:

To learn more about this feature, read Using Geospatial Charts (Maps), and Adding Geospatial Data.

Private VPC Access Preview
If you have data in AWS (perhaps in Amazon Redshift, Amazon Relational Database Service (RDS), or on EC2) or on-premises in Teradata or SQL Server on servers without public connectivity, this feature is for you. Private VPC Access for QuickSight uses an Elastic Network Interface (ENI) for secure, private communication with data sources in a VPC. It also allows you to use AWS Direct Connect to create a secure, private link with your on-premises resources. Here’s what it looks like:

If you are ready to join the preview, you can sign up today.

Jeff;

 

Community Profile: Matthew Timmons-Brown

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/community-profile-matthew-timmons-brown/

This column is from The MagPi issue 57. You can download a PDF of the full issue for free, or subscribe to receive the print edition in your mailbox or the digital edition on your tablet. All proceeds from the print and digital editions help the Raspberry Pi Foundation achieve its charitable goals.

“I first set up my YouTube channel because I noticed a massive lack of video tutorials for the Raspberry Pi,” explains Matthew Timmons-Brown, known to many as The Raspberry Pi Guy. At 18 years old, the Cambridge-based student has more than 60 000 subscribers to his channel, making his account the most successful Raspberry Pi–specific YouTube account to date.

Matthew Timmons-Brown

Matt gives a talk at the Raspberry Pi 5th Birthday weekend event

The Raspberry Pi Guy

If you’ve attended a Raspberry Pi event, there’s a good chance you’ve already met Matt. And if not, you’ll have no doubt come across one or more of his tutorials and builds online. On more than one occasion, his work has featured on the Raspberry Pi blog, with his yearly Raspberry Pi roundup videos being a staple of the birthday celebrations.

Matthew Timmons-Brown

With his website, Matt aimed to collect together “the many strands of The Raspberry Pi Guy” into one, neat, cohesive resource — and it works. From newcomers to the credit card-sized computer to hardened Pi veterans, The Raspberry Pi Guy offers aid and inspiration for many. Looking for a review of the Raspberry Pi Zero W? He’s filmed one. Looking for a step-by-step guide to building a Pi-powered Amazon Alexa? No problem, there’s one of those too.

Make your Raspberry Pi artificially intelligent! – Amazon Alexa Personal Assistant Tutorial

Artificial Intelligence. A hefty topic that has dominated the field since computers were first conceived. What if I told you that you could put an artificial intelligence service on your own $30 computer?! That’s right! In this tutorial I will show you how to create your own artificially intelligent personal assistant, using Amazon’s Alexa voice recognition and information service!

Raspberry Pi electric skateboard

Last summer, Matt introduced the world to his Raspberry Pi-controlled electric skateboard, soon finding himself plastered over local press as well as the BBC and tech sites like Adafruit and geek.com. And there’s no question as to why the build was so popular. With YouTubers such as Casey Neistat increasing the demand for electric skateboards on a near-daily basis, the call for a cheaper, home-brew version has quickly grown.

DIY 30KM/H ELECTRIC SKATEBOARD – RASPBERRY PI/WIIMOTE POWERED

Over the summer, I made my own electric skateboard using a £4 Raspberry Pi Zero. Controlled with a Nintendo Wiimote, capable of going 30km/h, and with a range of over 10km, this project has been pretty darn fun. In this video, you see me racing around Cambridge and I explain the ins and outs of this project.

Using a Raspberry Pi Zero, a Nintendo Wii Remote, and a little help from members of the Cambridge Makespace community, Matt built a board capable of reaching 30km/h, with a battery range of 10km per charge. Alongside Neistat, Matt attributes the project inspiration to Australian student Tim Maier, whose build we previously covered in The MagPi.

Matthew Timmons-Brown and Eben Upton standing in a car park looking at a smartphone

LiDAR

Despite the success and the fun of the electric skateboard (including convincing Raspberry Pi Trading CEO Eben Upton to have a go for local television news coverage), the project Matt is most proud of is his wireless LiDAR system for theoretical use on the Mars rovers.

Matthew Timmons-Brown's LiDAR project for scanning terrains with lasers

Using a tablet app to define the angles, Matt’s A Level coursework LiDAR build scans the surrounding area, returning the results to the touchscreen, where they can be manipulated by the user. With his passion for the cosmos and the International Space Station, it’s no wonder that this is Matt’s proudest build.

Built for his A Level Computer Science coursework, the build demonstrates Matt’s passion for space and physics. Used as a means of surveying terrain, LiDAR uses laser light to measure distance, allowing users to create 3D-scanned, high-resolution maps of a specific area. It is a perfect technology for exploring unknown worlds.

Matthew Timmons-Brown and two other young people at a reception in the Houses of Parliament

Matt was invited to St James’s Palace and the Houses of Parliament as part of the Raspberry Pi community celebrations in 2016

Joining the community

In a recent interview at Hills Road Sixth Form College, where he is studying mathematics, further mathematics, physics, and computer science, Matt revealed where his love of electronics and computer science started. “I originally became interested in computer science in 2012, when I read a tiny magazine article about a computer that I would be able to buy with pocket money. This was a pretty exciting thing for a 12-year-old! Your own computer… for less than £30?!” He went on to explain how it became his mission to learn all he could on the subject and how, months later, his YouTube channel came to life, cementing him firmly into the Raspberry Pi community

The post Community Profile: Matthew Timmons-Brown appeared first on Raspberry Pi.

Visualize AWS Cloudtrail Logs using AWS Glue and Amazon Quicksight

Post Syndicated from Luis Caro Perez original https://aws.amazon.com/blogs/big-data/streamline-aws-cloudtrail-log-visualization-using-aws-glue-and-amazon-quicksight/

Being able to easily visualize AWS CloudTrail logs gives you a better understanding of how your AWS infrastructure is being used. It can also help you audit and review AWS API calls and detect security anomalies inside your AWS account. To do this, you must be able to perform analytics based on your CloudTrail logs.

In this post, I walk through using AWS Glue and AWS Lambda to convert AWS CloudTrail logs from JSON to a query-optimized format dataset in Amazon S3. I then use Amazon Athena and Amazon QuickSight to query and visualize the data.

Solution overview

To process CloudTrail logs, you must implement the following architecture:

CloudTrail delivers log files in an Amazon S3 bucket folder. To correctly crawl these logs, you modify the file contents and folder structure using an Amazon S3-triggered Lambda function that stores the transformed files in an S3 bucket single folder. When the files are in a single folder, AWS Glue scans the data, converts it into Apache Parquet format, and catalogs it to allow for querying and visualization using Amazon Athena and Amazon QuickSight.

Walkthrough

Let’s look at the steps that are required to build the solution.

Set up CloudTrail logs

First, you need to set up a trail that delivers log files to an S3 bucket. To create a trail in CloudTrail, follow the instructions in Creating a Trail.

When you finish, the trail settings page should look like the following screenshot:

In this example, I set up log files to be delivered to the cloudtraillfcaro bucket.

Consolidate CloudTrail reports into a single folder using Lambda

AWS CloudTrail delivers log files using the following folder structure inside the configured Amazon S3 bucket:

AWSLogs/ACCOUNTID/CloudTrail/REGION/YEAR/MONTH/HOUR/filename.json.gz

Additionally, log files have the following structure:

{
    "Records": [{
        "eventVersion": "1.01",
        "userIdentity": {
            "type": "IAMUser",
            "principalId": "AIDAJDPLRKLG7UEXAMPLE",
            "arn": "arn:aws:iam::123456789012:user/Alice",
            "accountId": "123456789012",
            "accessKeyId": "AKIAIOSFODNN7EXAMPLE",
            "userName": "Alice",
            "sessionContext": {
                "attributes": {
                    "mfaAuthenticated": "false",
                    "creationDate": "2014-03-18T14:29:23Z"
                }
            }
        },
        "eventTime": "2014-03-18T14:30:07Z",
        "eventSource": "cloudtrail.amazonaws.com",
        "eventName": "StartLogging",
        "awsRegion": "us-west-2",
        "sourceIPAddress": "72.21.198.64",
        "userAgent": "signin.amazonaws.com",
        "requestParameters": {
            "name": "Default"
        },
        "responseElements": null,
        "requestID": "cdc73f9d-aea9-11e3-9d5a-835b769c0d9c",
        "eventID": "3074414d-c626-42aa-984b-68ff152d6ab7"
    },
    ... additional entries ...
    ]

If AWS Glue crawlers are used to catalog these files as they are written, the following obstacles arise:

  1. AWS Glue identifies different tables per different folders because they don’t follow a traditional partition format.
  2. Based on the structure of the file content, AWS Glue identifies the tables as having a single column of type array.
  3. CloudTrail logs have JSON attributes that use uppercase letters. According to the Best Practices When Using Athena with AWS Glue, it is recommended that you convert these to lowercase.

To have AWS Glue catalog all log files in a single table with all the columns describing each event, implement the following Lambda function:

from __future__ import print_function
import json
import urllib
import boto3
import gzip

s3 = boto3.resource('s3')
client = boto3.client('s3')

def convertColumntoLowwerCaps(obj):
    for key in obj.keys():
        new_key = key.lower()
        if new_key != key:
            obj[new_key] = obj[key]
            del obj[key]
    return obj


def lambda_handler(event, context):

    bucket = event['Records'][0]['s3']['bucket']['name']
    key = urllib.unquote_plus(event['Records'][0]['s3']['object']['key'].encode('utf8'))
    print(bucket)
    print(key)
    try:
        newKey = 'flatfiles/' + key.replace("/", "")
        client.download_file(bucket, key, '/tmp/file.json.gz')
        with gzip.open('/tmp/out.json.gz', 'w') as output, gzip.open('/tmp/file.json.gz', 'rb') as file:
            i = 0
            for line in file: 
                for record in json.loads(line,object_hook=convertColumntoLowwerCaps)['records']:
            		if i != 0:
            		    output.write("\n")
            		output.write(json.dumps(record))
            		i += 1
        client.upload_file('/tmp/out.json.gz', bucket,newKey)
        return "success"
    except Exception as e:
        print(e)
        print('Error processing object {} from bucket {}. Make sure they exist and your bucket is in the same region as this function.'.format(key, bucket))
        raise e

The function goes over each element of the records array, changes uppercase letters to lowercase in column names, and inserts each element of the array as a single line of a new file. The new file is saved inside a flatfiles folder created by the function without any subfolders in the S3 bucket.

The function should have a role containing a policy with at least the following permissions:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Action": [
                "s3:*"
            ],
            "Resource": [
                "arn:aws:s3:::cloudtraillfcaro/*",
                "arn:aws:s3:::cloudtraillfcaro"
            ],
            "Effect": "Allow"
        }
    ]
}

In this example, CloudTrail delivers logs to the cloudtraillfcaro bucket. Make sure that you replace this name with your bucket name in the policy. For more information about how to work with inline policies, see Working with Inline Policies.

After the Lambda function is created, you can set up the following trigger using the Triggers tab on the AWS Lambda console.

Choose Add trigger, and choose S3 as a source of the trigger.

After choosing the source, configure the following settings:

In the trigger, any file that is written to the path for the log files—which in this case is AWSLogs/119582755581/CloudTrail/—is processed. Make sure that the Enable trigger check box is selected and that the bucket and prefix parameters match your use case.

After you set up the function and receive log files, the bucket (in this case cloudtraillfcaro) should contain the processed files inside the flatfiles folder.

Catalog source data

Once the files are processed by the Lambda function, set up a crawler named cloudtrail to catalog them.

The crawler must point to the flatfiles folder.

All the crawlers and AWS Glue jobs created for this solution must have a role with the AWSGlueServiceRole managed policy and an inline policy with permissions to modify the S3 buckets used on the Lambda function. For more information, see Working with Managed Policies.

The role should look like the following:

In this example, the inline policy named s3perms contains the permissions to modify the S3 buckets.

After you choose the role, you can schedule the crawler to run on demand.

A new database is created, and the crawler is set to use it. In this case, the cloudtrail database is used for all the tables.

After the crawler runs, a single table should be created in the catalog with the following structure:

The table should contain the following columns:

Create and run the AWS Glue job

To convert all the CloudTrail logs to a columnar store in Parquet, set up an AWS Glue job by following these steps.

Upload the following script into a bucket in Amazon S3:

import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
import boto3
import time

## @params: [JOB_NAME]
args = getResolvedOptions(sys.argv, ['JOB_NAME'])

sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)

datasource0 = glueContext.create_dynamic_frame.from_catalog(database = "cloudtrail", table_name = "flatfiles", transformation_ctx = "datasource0")
resolvechoice1 = ResolveChoice.apply(frame = datasource0, choice = "make_struct", transformation_ctx = "resolvechoice1")
relationalized1 = resolvechoice1.relationalize("trail", args["TempDir"]).select("trail")
datasink = glueContext.write_dynamic_frame.from_options(frame = relationalized1, connection_type = "s3", connection_options = {"path": "s3://cloudtraillfcaro/parquettrails"}, format = "parquet", transformation_ctx = "datasink4")
job.commit()

In the example, you load the script as a file named cloudtrailtoparquet.py. Make sure that you modify the script and update the “{"path": "s3://cloudtraillfcaro/parquettrails"}” with the destination in which you want to store your results.

After uploading the script, add a new AWS Glue job. Choose a name and role for the job, and choose the option of running the job from An existing script that you provide.

To avoid processing the same data twice, enable the Job bookmark setting in the Advanced properties section of the job properties.

Choose Next twice, and then choose Finish.

If logs are already in the flatfiles folder, you can run the job on demand to generate the first set of results.

Once the job starts running, wait for it to complete.

When the job is finished, its Run status should be Succeeded. After that, you can verify that the Parquet files are written to the Amazon S3 location.

Catalog results

To be able to process results from Athena, you can use an AWS Glue crawler to catalog the results of the AWS Glue job.

In this example, the crawler is set to use the same database as the source named cloudtrail.

You can run the crawler using the console. When the crawler finishes running and has processed the Parquet results, a new table should be created in the AWS Glue Data Catalog. In this example, it’s named parquettrails.

The table should have the classification set to parquet.

It should have the same columns as the flatfiles table, with the exception of the struct type columns, which should be relationalized into several columns:

In this example, notice how the requestparameters column, which was a struct in the original table (flatfiles), was transformed to several columns—one for each key value inside it. This is done using a transformation native to AWS Glue called relationalize.

Query results with Athena

After crawling the results, you can query them using Athena. For example, to query what events took place in the time frame between 2017-10-23t12:00:00 and 2017-10-23t13:00, use the following select statement:

select *
from cloudtrail.parquettrails
where eventtime > '2017-10-23T12:00:00Z' AND eventtime < '2017-10-23T13:00:00Z'
order by eventtime asc;

Be sure to replace cloudtrail.parquettrails with the names of your database and table that references the Parquet results. Replace the datetimes with an hour when your account had activity and was processed by the AWS Glue job.

Visualize results using Amazon QuickSight

Once you can query the data using Athena, you can visualize it using Amazon QuickSight. Before connecting Amazon QuickSight to Athena, be sure to grant QuickSight access to Athena and the associated S3 buckets in your account. For more information, see Managing Amazon QuickSight Permissions to AWS Resources. You can then create a new data set in Amazon QuickSight based on the Athena table that you created.

After setting up permissions, you can create a new analysis in Amazon QuickSight by choosing New analysis.

Then add a new data set.

Choose Athena as the source.

Give the data source a name (in this case, I named it cloudtrail).

Choose the name of the database and the table referencing the Parquet results.

Then choose Visualize.

After that, you should see the following screen:

Now you can create some visualizations. First, search for the sourceipaddress column, and drag it to the AutoGraph section.

You can see a list of the IP addresses that you have used to interact with AWS. To review whether these IP addresses have been used from IAM users, internal AWS services, or roles, use the type value that is inside the useridentity field of the original log files. Thanks to the relationalize transformation, this value is available as the useridentity.type column. After the column is added into the Group/Color box, the visualization should look like the following:

You can now see and distinguish the most used IPs and whether they are used from roles, AWS services, or IAM users.

After following all these steps, you can use Amazon QuickSight to add different columns from CloudTrail and perform different types of visualizations. You can build operational dashboards that continuously monitor AWS infrastructure usage and access. You can share those dashboards with others in your organization who might need to see this data.

Summary

In this post, you saw how you can use a simple Lambda function and an AWS Glue script to convert text files into Parquet to improve Athena query performance and data compression. The post also demonstrated how to use AWS Lambda to preprocess files in Amazon S3 and transform them into a format that is recognizable by AWS Glue crawlers.

This example, used AWS CloudTrail logs, but you can apply the proposed solution to any set of files that after preprocessing, can be cataloged by AWS Glue.


Additional Reading

Learn how to Harmonize, Query, and Visualize Data from Various Providers using AWS Glue, Amazon Athena, and Amazon QuickSight.


About the Authors

Luis Caro is a Big Data Consultant for AWS Professional Services. He works with our customers to provide guidance and technical assistance on big data projects, helping them improving the value of their solutions when using AWS.

 

 

 

Tableau 10.4 Supports Amazon Redshift Spectrum with External Amazon S3 Tables

Post Syndicated from Robin Cottiss original https://aws.amazon.com/blogs/big-data/tableau-10-4-supports-amazon-redshift-spectrum-with-external-amazon-s3-tables/

This is a guest post by Robin Cottiss, strategic customer consultant, Russell Christopher, staff product manager, and Vaidy Krishnan, senior manager of product marketing, at Tableau. Tableau, in their own words, “helps anyone quickly analyze, visualize, and share information. More than 61,000 customer accounts get rapid results with Tableau in the office and on the go. Over 300,000 people use Tableau Public to share public data in their blogs and websites.”

We’re excited to announce today an update to our Amazon Redshift connector with support for Amazon Redshift Spectrum to analyze data in external Amazon S3 tables. This feature, the direct result of joint engineering and testing work performed by the teams at Tableau and AWS, was released as part of Tableau 10.3.3 and will be available broadly in Tableau 10.4.1. With this update, you can quickly and directly connect Tableau to data in Amazon Redshift and analyze it in conjunction with data in Amazon S3—all with drag-and-drop ease.

This connector is yet another in a series of market-leading integrations of Tableau with AWS’s analytics platform, with services such as Amazon Redshift, Amazon EMR, and Amazon Athena. These integrations have allowed Tableau to become the natural choice of tool for analyzing data stored on AWS. Beyond this, Tableau Server runs seamlessly in the AWS Cloud infrastructure. If you prefer to deploy all your applications inside AWS, you have a complete solution offering from Tableau.

How does support for Amazon Redshift Spectrum help you?

If you’re like many Tableau customers, you have large buckets of data stored in Amazon S3. You might need to access this data frequently and store it in a consistent, highly structured format. If so, you can provision it to a data warehouse like Amazon Redshift. You might also want to explore this S3 data on an ad hoc basis. For example, you might want to determine whether or not to provision the data, and where—options might be Hadoop, Impala, Amazon EMR, or Amazon Redshift. To do so, you can use Amazon Athena, a serverless interactive query service from AWS that requires no infrastructure setup and management.

But what if you want to analyze both the frequently accessed data stored locally in Amazon Redshift AND your full datasets stored cost-effectively in Amazon S3? What if you want the throughput of disk and sophisticated query optimization of Amazon Redshift AND a service that combines a serverless scale-out processing capability with the massively reliable and scalable S3 infrastructure? What if you want the super-fast performance of Amazon Redshift AND support for open storage formats (for example, Parquet or ORC) in S3?

To enable these AND and resolve the tyranny of ORs, AWS launched Amazon Redshift Spectrum earlier this year.

Amazon Redshift Spectrum gives you the freedom to store your data where you want, in the format you want, and have it available for processing when you need it. Since the Amazon Redshift Spectrum launch, Tableau has worked tirelessly to provide best-in-class support for this new service. With Tableau and Redshift Spectrum, you can extend your Amazon Redshift analyses out to the entire universe of data in your S3 data lakes.

This latest update has been tested by many customers with very positive feedback. One such customer is the world’s largest food product distributor, Sysco—you can watch their session referencing the Amazon Spectrum integration at Tableau Conference 2017. Sysco also plans to reprise its “Tableau on AWS” story again in a month’s time at AWS re:Invent.

Now, I’d like to use a concrete example to demonstrate how Tableau works with Amazon Redshift Spectrum. In this example, I also show you how and why you might want to connect to your AWS data in different ways.

The setup

I use the pipeline described following to ingest, process, and analyze data with Tableau on an AWS stack. The source data is the New York City Taxi dataset, which has 9 years’ worth of taxi rides activity (including pick-up and drop-off location, amount paid, payment type, and so on) captured in 1.2 billion records.

In this pipeline, this data lands in S3, is cleansed and partitioned by using Amazon EMR, and is then converted to a columnar Parquet format that is analytically optimized. You can point Tableau to the raw data in S3 by using Amazon Athena. You can also access the cleansed data with Tableau using Presto through your Amazon EMR cluster.

Why use Tableau this early in the pipeline? Because sometimes you want to understand what’s there and what questions are worth asking before you even start the analysis.

After you find out what those questions are and determine if this sort of analysis has long-term usefulness, you can automate and optimize that pipeline. You do this to add new data as soon as possible as it arrives, to get it to the processes and people that need it. You might also want to provision this data to a highly performant “hotter” layer (Amazon Redshift or Tableau Extract) for repeated access.

In the illustration preceding, S3 contains the raw denormalized ride data at the timestamp level of granularity. This S3 data is the fact table. Amazon Redshift has the time dimensions broken out by date, month, and year, and also has the taxi zone information.

Now imagine I want to know where and when taxi pickups happen on a certain date in a certain borough. With support for Amazon Redshift Spectrum, I can now join the S3 tables with the Amazon Redshift dimensions, as shown following.

I can next analyze the data in Tableau to produce a borough-by-borough view of New York City ride density on Christmas Day 2015.

Or I can hone in on just Manhattan and identify pickup hotspots, with ride charges way above the average!

With Amazon Redshift Spectrum, you now have a fast, cost-effective engine that minimizes data processed with dynamic partition pruning. You can further improve query performance by reducing the data scanned. You do this by partitioning and compressing data and by using a columnar format for storage.

At the end of the day, which engine you use behind Tableau is a function of what you want to optimize for. Some possible engines are Amazon Athena, Amazon Redshift, and Redshift Spectrum, or you can bring a subset of data into Tableau Extract. Factors in planning optimization include these:

  • Are you comfortable with the serverless cost model of Amazon Athena and potential full scans? Or do you prefer the advantages of no setup?
  • Do you want the throughput of local disk?
  • Effort and time of setup. Are you okay with the lead-time of an Amazon Redshift cluster setup, as opposed to just bringing everything into Tableau Extract?

To meet the many needs of our customers, Tableau’s approach is simple: It’s all about choice. The choice of how you want to connect to and analyze your data. Throughout the history of our product and into the future, we have and will continue to empower choice for customers.

For more on how to deal with choice, as you go about making architecture decisions for your enterprise, watch this big data strategy session my friend Robin Cottiss and I delivered at Tableau Conference 2017. This session includes several customer examples leveraging the Tableau on AWS platform, and also a run-through of the aforementioned demonstration.

If you’re curious to learn more about analyzing data with Tableau on Amazon Redshift we encourage you to check out the following resources: