Tag Archives: Token

Researchers Use a Blockchain to Boost Anonymous Torrent Sharing

Post Syndicated from Ernesto original https://torrentfreak.com/researchers-use-a-blockchain-to-boost-anonymous-torrent-sharing-180129/

The Tribler client has been around for over a decade. We first covered it in 2006 and since then it’s developed into a truly decentralized BitTorrent client.

Even if all torrent sites were shut down today, Tribler users would still be able to find and add new content.

The project is not run by regular software developers but by a team of quality researchers at Delft University of Technology. There are currently more than 45 masters students, various thesis students, five dedicated scientific developers, and several professors involved.

Simply put, Triber aims to make the torrent ecosystem truly decentralized and anonymous. A social network of peers that can survive even if all torrent sites ceased to exist.

“Search and download torrents with less worries or censorship,” Triber’s tagline reads.

Like many other BitTorrent clients, Tribler has a search box at the top of the application. However, the search results that appear when users type in a keyword don’t come from a central index. Instead, they come directly from other peers.

Thriber’s search results

With the latest release, Tribler 7.0, the project adds another element to the mix, it’s very own blockchain. This blockchain keeps track of how much people are sharing and rewards them accordingly.

“Tribler is a torrent client for social people, who help each other. You can now earn tokens by helping others. It is specifically designed to prevent freeriding and detect hit-and-run peers.” Tribler leader Dr. Johan Pouwelse tells TF.

“You help other Tribler users by seeding and by enhancing their privacy. In return, you get faster downloads, as your tokens show you contribute to the community.”

Pouwelse, who aims to transform BitTorrent into an ethical Darknet, just presented the latest release at Stanford University. In addition, the Internet Engineering Task Force is also considering the blockchain implementation as an official Internet standard.

This recognition from academics and technology experts is welcome, of course, but Triber’s true power comes from the users. The client has gathered a decent userbase of the years but there sure is plenty room for improvement on this front.

The anonymity aspect is perhaps one of the biggest selling points and Pouwelse believes that this will greatly benefit from the blockchain implementation.

Triber provides users with pseudo anonymity by routing the transfers through other users. However, this means that the amount of bandwith used by the application inceases as well. Thus far, this hasn’t worked very well, which resulted in slow anonymous downloads.

“With the integrated blockchain release today we think we can start fixing the problem of both underseeded swarms and fast proxies,” Dr. Pouwelse says.

“Our solution is basically very simple, only social people get decent performance on Tribler. This means in a few years we will end up with only users that act nice. Others leave.”

Tribler’s trust stats

Tribler provides users with quite a bit of flexibility on the anonymity site. The feature can be turned off completely, or people can choose a protection layer ranging from one to four hops.

What’s also important to note is that users don’t operate as exit nodes by default. The IP-addresses of the exit nodes are public ouitside the network and can be monitored, so that would only increase liability.

So who are the exit-nodes in this process then? According to Pouwelse’s rather colorful description, these appear to be volunteers that run their code through a VPN a or a VPS server.

“The past years we have created an army of bots we call ‘Self-replicating Autonomous Entities’. These are Terminator-style self-replicating pieces of code which have their own Bitcoin wallet to go out there and buy servers to run more copies of themselves,” he explains.

“They utilize very primitive genetic evolution to improve survival, buy a VPN for protection, earn credits using our experimental credit mining preview release, and sell our bandwidth tokens on our integrated decentral market for cold hard Bitcoin cash to renew the cycle of life for the next month billing cycle of their VPS provider.”

Some might question why there’s such a massive research project dedicated to building an anonymous BitTorrent network. What are the benefits to society?

The answer is clear, according to Pouwelse. The ethical darknet they envision will be a unique micro-economy where sharing is rewarded, without having to expose one’s identity.

“We are building the Internet of Trust. The Internet can do amazing things, it even created honesty among drugs dealers,” he says, referring to the infamous Silk Road.

“Reliability rating of drugs lords gets you life imprisonment. That’s not something we want. We are creating our own trustworthy micro-economy for bandwidth tokens and real Bitcoins,” he adds.

People who are interested in taking Tribler for a spin can download the latest version from the official website.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN discounts, offers and coupons

Backblaze B2 Supports CORS for Cross Origin Resource Sharing

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/enable-cors-for-cross-origin-resource-sharing/

Host files between domains with B2 CORS Rules

Web pages do their magic by loading assets such as images, videos, fonts, text, and other resources from one or more servers on the internet. Most often, data for a website is stored on the same server where the webpages themselves are stored. Sometimes, though, websites will pull in data from servers located elsewhere on the internet.

Allowing websites to include data from other servers can pose possible security risks. To protect users, web browsers enforce security policies that allow scripts in one web page to access data in a second web page only if both web pages have the same origin (i.e. server). This prevents a malicious or faulty script on one page from obtaining access to data on another page that it shouldn’t.

There are many times, however, when one might want to load assets hosted on other servers across the internet. Resources such as fonts, videos, style sheets, images, and iframes are commonly loaded from other origins. It’s great to restrict access to content that might be unauthorized or dangerous, but the web developer needs to be able to specify when it’s okay to load a resource from a different origin.

That’s where CORS comes in.

What is CORS?

To enable web pages to load content that is stored in a different origin, W3C (World Wide Web Consortium), the international community that develops open standards to ensure the long-term growth of the Web, created the Cross-Origin Resource Sharing (CORS) mechanism that allows web pages to access data with a different origin.

The web page might be located on one origin, e.g.


And some data the web page loads might be located on a different origin, e.g.


CORS requires that the resource server explicitly declare that it’s OK to load the asset from a different origin. The browser accomplishes this by making a “preflight” request to ask the server whether it’s OK to make the cross-origin request. By default, servers will say “no” to preflight requests. Rules must be put into place to enable the server to reply to these preflight requests saying it’s OK to serve the asset to a different origin.

B2 Supports CORS for Cross Origin Resource Sharing

B2 is Backblaze’s general purpose cloud storage that can include any type of data that can be stored in the cloud. With pricing that’s ¼ of Amazon’s S3, web developers use B2 as an origin for web data, including text, numbers, scripts, fonts, images, stylesheets, iframes, and videos.

Backblaze supports the standard CORS mechanism that allows B2 customers to share the content of their buckets with web pages hosted in origins other than B2.

In keeping with CORS practices, B2 servers will say “no” to preflight requests to protect the unauthorized sharing of assets to other origins. Adding CORS rules to your bucket tells B2 which preflight requests to approve. CORS is a security feature that is in addition to normal B2 authorization mechanisms. Requests will still need to present normal B2 authorization tokens to download content from non-public buckets.

B2 Cloud Storage Buckets dialog

B2 Cloud Storage Buckets dialog

CORS Rules for BzFileShare

B2 CORS Rules settings dialog

Learn More about B2 and CORS

You can read all about B2’s support of CORS, and how to add rules to your B2 buckets to serve web assets cross-origin, on Backblaze’s website at CORS: Cross-Origin Resource Sharing.

The post Backblaze B2 Supports CORS for Cross Origin Resource Sharing appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

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:


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:


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")

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:


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.


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")))

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.


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))

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")

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)

        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")

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.


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.




Blockchain Startup White Rabbit Calls on Pirate Sites to Do Business, Legally

Post Syndicated from Andy original https://torrentfreak.com/blockchain-startup-white-rabbit-calls-on-pirate-sites-to-do-business-legally-180102/

For as long as piracy has been mainstream, people have tried to find ways to monetize the system. While many have had good intentions, only models focusing on the negative (copyright trolling, for example) have enjoyed any level of success.

Blockchain startup White Rabbit is hoping to buck that trend but it’s not going to be easy. Then again, nothing worthwhile is, so what do they have to offer?

White Rabbit begins with the assumption that while they love their pirate sites, a many as 60% of pirates would happily reward creators if it was made easy enough. The startup deals with this by inviting pirates to carry on using the kinds of unauthorized sites and services they’re using already, but with a twist.

By installing the White Rabbit browser plug-in, the company will be able to see what content the user is accessing. It will then attempt to match that download to deals it’s made with the companies behind those movies or TV shows. They’ll then get paid a set amount.

“White Rabbit is a content ecosystem accessed through a plugin that recognizes the film and series you stream. The streaming sites are P2P or open server, meaning users can choose where they want to stream,” White Rabbit CEO Alan R. Milligan informs TF.

“We already have a library of films that have won and been nominated for Oscars, Cannes, Berlin and Venice film festival best film prizes – but will continue adding more films and series as we near launch.”

It’s envisioned that this mechanism will prove popular with reluctant pirates since instead of paying Netflix, Amazon, and dozens of other services, users can pay for content through one channel. And, since White Rabbit uses blockchain technology, rights holders can be ensured complete financial transparency, with user payments going straight to them without delay, cutting out the middleman.

“Users are anonymous but can offer filmmakers, artists or other content right holders (investors, distributors, sales agents) our tokens (WRT) as good faith that they are willing to pay for the content. Should the rights holders accept, we enter into a contract with the rights holder that allows them to receive revenue – and accept P2P streaming. We find, and research shows, that most people that are forced to piracy [do so] because they are just not able to access content,” Milligan adds.

White Rabbit’s CEO, who is a filmmaker himself, also sees opportunities to bring fans and filmmakers closer together. Once users have paid for content, they continue to get access via something called the Rabbit Hole, an interface which provides extras that are normally found on a DVD, such as deleted scenes etc.

The team behind White Rabbit describe themselves as “responsible rebels” hoping to spark a revolution. While that’s clearly the goal, by any measure there is a mountain to climb, not least on the content front.

When TorrentFreak first started speaking with the startup in October last year, we were told they were “closing in on 500 films” with contracts, although they wouldn’t elaborate on who might be on board. Nevertheless, that is quite a lot of movies, especially given the mainstream studios’ hatred of pirate sites and anything they might be involved in.

However, subsequent discussion suggests that those with more niche tastes might be White Rabbit’s initial target audience.

“I believe timing is of big relevance and right now a lot of producers are scared of where they´re going to go now that Netflix is enforcing its 50/50 policy. There are also so many amazing films out there that get no or little digital distribution at all,” Milligan says.

“As a Norwegian film producer there is little chance of the film being streamed in my home country – even if we won awards in Cannes and Venice. My latest film Valley of Shadows got US digital distribution, but in Norway – nada.

“My colleagues around the world are suffering the same way, not to mention all the fans who cant watch local films and series. So the indie part of the industry – which is most of us (and still representing 20-30% of cinema sales) – are very ready for change.”

But while indie producers could benefit nicely from White Rabbit, Milligan highlights problems that the big studios have, and suggests that they might like to see the startup succeed too.

“The studios will likely want to see our business model work – but they also have a problem with Netflix which has become a studio. So they´re competitors now, but Netflix has a 100M subscriber advantage. Will they all break out and create each their streaming site for their content only? That would be terrible for fans,” he notes.

That would indeed be a huge problem and it’s an issue we’ve raised here on TF on several occasions. However, if White Rabbit is to succeed, it needs to overcome significant hurdles. We raised just a handful of these with its CEO. First up, Partner Streaming Sites (PSS).

PSS sites appear to be pirate sites that will partner with White Rabbit, so the latter can tap into the formers’ userbases. When White Rabbit users stream ‘pirate’ content from a PSS, that content will be monetized, with the creator getting paid quickly and transparently. At that point, it seems, the content will become non-infringing.

But while that sounds intriguing in theory, plenty of questions remain. White Rabbit says it will share “up to $1M” from its token sale “with the most innovative, brand conscious, film and series loving streaming sites either already out there, planned or about to launch.”

The start-up says the best projects could get $100,000 each but, since its goal is to convert pirates, that necessarily means doing business with pirate sites.

So we asked; how will it be possible to do business with people that are regularly described as criminals? How will it then become possible to secure deals with filmmakers that will undoubtedly come under huge pressure from industry players not to participate in the White Rabbit scheme?

“What we are trying to do is to change digital distribution to everyone´s benefit. We have no interest in financing illegal content, we are interested in spurring innovation in streaming, access for fans and due payment for the rights holders,” Milligan explains.

“That´s what PSS can help us achieve using the WRT (White Rabbit Token) – that helps us find out who wants to be part of this model. No revenue exchanges hands until rights holders accept the token. What is important for rights holders is that we generate more revenue for them than current business models, and we haven´t even included the Rabbit Hole revenue yet.”

So what happens if a White Rabbit user tries to stream something that isn’t part of the program? According to Milligan, PSS sites must remove the content and let White Rabbit users know they must get the content legally elsewhere.

Clearly, the vast majority of pirate site users aren’t White Rabbit users now, nor will they be so in the future, so the removal of content is massively counter-productive for pirate sites. Indeed, it’s this reluctance to take down infringing content that causes them most of their problems.

So, hypothetically, what happens when the operators of streaming site X (that previously partnered with White Rabbit) get arrested and their site shut down for distributing Hollywood content that isn’t part of the program?

“PSS´s would never distribute illegal content, we are offering an opportunity to monetize. We are allowing a platform to those that see monetized P2P as beneficial to their income stream,” Milligan says.

“Hollywood is tricky though, I admit. The proof is in the pudding, so if we have to prove the value through indie and arthouse films first that´s OK. That is still 30% of the multi-billion dollar film market, so we are OK to start with that.”

The final issue is the price and where revenue goes. White Rabbit envisions a user paying $2 for film and $1 for a TV show, although producers are free to set their own price. That means 11 TV shows or five movies per month, given the Netflix model/budget of roughly $11.00 for the same period.

Revenue generated would then be split, with 75% going to the rightsholders, 15% to White Rabbit, and 10% to PSS sites. There’s also a provision for non-PSS sites to be a part of the program, but they would only get 5%, with the remaining 5% going to White Rabbit.

With an incredibly ambitious project like this, it’s easy to find reasons why it might not succeed or even fail to get off the ground. But the team behind the operation have lots of experience in relevant fields and from what we’ve seen are putting considerable effort into getting things moving, as their white paper (pdf) explains.

Currently, White Rabbit is seeking conversation with prospective Partner Streaming Sites, who will provide the content on which White Rabbit will survive. It will certainly be interesting to see which sites put themselves forward for consideration.

This is one of those projects that raises a dizzying volume of questions, with each living up to their billing as part of the Rabbit Hole. The big question is whether the Rabbit Hole will eventually lead to Wonderland or will render everyone who ventures inside feeling surreal and disorientated.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN discounts, offers and coupons

Power data ingestion into Splunk using Amazon Kinesis Data Firehose

Post Syndicated from Tarik Makota original https://aws.amazon.com/blogs/big-data/power-data-ingestion-into-splunk-using-amazon-kinesis-data-firehose/

In late September, during the annual Splunk .conf, Splunk and Amazon Web Services (AWS) jointly announced that Amazon Kinesis Data Firehose now supports Splunk Enterprise and Splunk Cloud as a delivery destination. This native integration between Splunk Enterprise, Splunk Cloud, and Amazon Kinesis Data Firehose is designed to make AWS data ingestion setup seamless, while offering a secure and fault-tolerant delivery mechanism. We want to enable customers to monitor and analyze machine data from any source and use it to deliver operational intelligence and optimize IT, security, and business performance.

With Kinesis Data Firehose, customers can use a fully managed, reliable, and scalable data streaming solution to Splunk. In this post, we tell you a bit more about the Kinesis Data Firehose and Splunk integration. We also show you how to ingest large amounts of data into Splunk using Kinesis Data Firehose.

Push vs. Pull data ingestion

Presently, customers use a combination of two ingestion patterns, primarily based on data source and volume, in addition to existing company infrastructure and expertise:

  1. Pull-based approach: Using dedicated pollers running the popular Splunk Add-on for AWS to pull data from various AWS services such as Amazon CloudWatch or Amazon S3.
  2. Push-based approach: Streaming data directly from AWS to Splunk HTTP Event Collector (HEC) by using AWS Lambda. Examples of applicable data sources include CloudWatch Logs and Amazon Kinesis Data Streams.

The pull-based approach offers data delivery guarantees such as retries and checkpointing out of the box. However, it requires more ops to manage and orchestrate the dedicated pollers, which are commonly running on Amazon EC2 instances. With this setup, you pay for the infrastructure even when it’s idle.

On the other hand, the push-based approach offers a low-latency scalable data pipeline made up of serverless resources like AWS Lambda sending directly to Splunk indexers (by using Splunk HEC). This approach translates into lower operational complexity and cost. However, if you need guaranteed data delivery then you have to design your solution to handle issues such as a Splunk connection failure or Lambda execution failure. To do so, you might use, for example, AWS Lambda Dead Letter Queues.

How about getting the best of both worlds?

Let’s go over the new integration’s end-to-end solution and examine how Kinesis Data Firehose and Splunk together expand the push-based approach into a native AWS solution for applicable data sources.

By using a managed service like Kinesis Data Firehose for data ingestion into Splunk, we provide out-of-the-box reliability and scalability. One of the pain points of the old approach was the overhead of managing the data collection nodes (Splunk heavy forwarders). With the new Kinesis Data Firehose to Splunk integration, there are no forwarders to manage or set up. Data producers (1) are configured through the AWS Management Console to drop data into Kinesis Data Firehose.

You can also create your own data producers. For example, you can drop data into a Firehose delivery stream by using Amazon Kinesis Agent, or by using the Firehose API (PutRecord(), PutRecordBatch()), or by writing to a Kinesis Data Stream configured to be the data source of a Firehose delivery stream. For more details, refer to Sending Data to an Amazon Kinesis Data Firehose Delivery Stream.

You might need to transform the data before it goes into Splunk for analysis. For example, you might want to enrich it or filter or anonymize sensitive data. You can do so using AWS Lambda. In this scenario, Kinesis Data Firehose buffers data from the incoming source data, sends it to the specified Lambda function (2), and then rebuffers the transformed data to the Splunk Cluster. Kinesis Data Firehose provides the Lambda blueprints that you can use to create a Lambda function for data transformation.

Systems fail all the time. Let’s see how this integration handles outside failures to guarantee data durability. In cases when Kinesis Data Firehose can’t deliver data to the Splunk Cluster, data is automatically backed up to an S3 bucket. You can configure this feature while creating the Firehose delivery stream (3). You can choose to back up all data or only the data that’s failed during delivery to Splunk.

In addition to using S3 for data backup, this Firehose integration with Splunk supports Splunk Indexer Acknowledgments to guarantee event delivery. This feature is configured on Splunk’s HTTP Event Collector (HEC) (4). It ensures that HEC returns an acknowledgment to Kinesis Data Firehose only after data has been indexed and is available in the Splunk cluster (5).

Now let’s look at a hands-on exercise that shows how to forward VPC flow logs to Splunk.

How-to guide

To process VPC flow logs, we implement the following architecture.

Amazon Virtual Private Cloud (Amazon VPC) delivers flow log files into an Amazon CloudWatch Logs group. Using a CloudWatch Logs subscription filter, we set up real-time delivery of CloudWatch Logs to an Kinesis Data Firehose stream.

Data coming from CloudWatch Logs is compressed with gzip compression. To work with this compression, we need to configure a Lambda-based data transformation in Kinesis Data Firehose to decompress the data and deposit it back into the stream. Firehose then delivers the raw logs to the Splunk Http Event Collector (HEC).

If delivery to the Splunk HEC fails, Firehose deposits the logs into an Amazon S3 bucket. You can then ingest the events from S3 using an alternate mechanism such as a Lambda function.

When data reaches Splunk (Enterprise or Cloud), Splunk parsing configurations (packaged in the Splunk Add-on for Kinesis Data Firehose) extract and parse all fields. They make data ready for querying and visualization using Splunk Enterprise and Splunk Cloud.


Install the Splunk Add-on for Amazon Kinesis Data Firehose

The Splunk Add-on for Amazon Kinesis Data Firehose enables Splunk (be it Splunk Enterprise, Splunk App for AWS, or Splunk Enterprise Security) to use data ingested from Amazon Kinesis Data Firehose. Install the Add-on on all the indexers with an HTTP Event Collector (HEC). The Add-on is available for download from Splunkbase.

HTTP Event Collector (HEC)

Before you can use Kinesis Data Firehose to deliver data to Splunk, set up the Splunk HEC to receive the data. From Splunk web, go to the Setting menu, choose Data Inputs, and choose HTTP Event Collector. Choose Global Settings, ensure All tokens is enabled, and then choose Save. Then choose New Token to create a new HEC endpoint and token. When you create a new token, make sure that Enable indexer acknowledgment is checked.

When prompted to select a source type, select aws:cloudwatch:vpcflow.

Create an S3 backsplash bucket

To provide for situations in which Kinesis Data Firehose can’t deliver data to the Splunk Cluster, we use an S3 bucket to back up the data. You can configure this feature to back up all data or only the data that’s failed during delivery to Splunk.

Note: Bucket names are unique. Thus, you can’t use tmak-backsplash-bucket.

aws s3 create-bucket --bucket tmak-backsplash-bucket --create-bucket-configuration LocationConstraint=ap-northeast-1

Create an IAM role for the Lambda transform function

Firehose triggers an AWS Lambda function that transforms the data in the delivery stream. Let’s first create a role for the Lambda function called LambdaBasicRole.

Note: You can also set this role up when creating your Lambda function.

$ aws iam create-role --role-name LambdaBasicRole --assume-role-policy-document file://TrustPolicyForLambda.json

Here is TrustPolicyForLambda.json.

  "Version": "2012-10-17",
  "Statement": [
      "Effect": "Allow",
      "Principal": {
        "Service": "lambda.amazonaws.com"
      "Action": "sts:AssumeRole"


After the role is created, attach the managed Lambda basic execution policy to it.

$ aws iam attach-role-policy 
  --policy-arn arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole 
  --role-name LambdaBasicRole


Create a Firehose Stream

On the AWS console, open the Amazon Kinesis service, go to the Firehose console, and choose Create Delivery Stream.

In the next section, you can specify whether you want to use an inline Lambda function for transformation. Because incoming CloudWatch Logs are gzip compressed, choose Enabled for Record transformation, and then choose Create new.

From the list of the available blueprint functions, choose Kinesis Data Firehose CloudWatch Logs Processor. This function unzips data and place it back into the Firehose stream in compliance with the record transformation output model.

Enter a name for the Lambda function, choose Choose an existing role, and then choose the role you created earlier. Then choose Create Function.

Go back to the Firehose Stream wizard, choose the Lambda function you just created, and then choose Next.

Select Splunk as the destination, and enter your Splunk Http Event Collector information.

Note: Amazon Kinesis Data Firehose requires the Splunk HTTP Event Collector (HEC) endpoint to be terminated with a valid CA-signed certificate matching the DNS hostname used to connect to your HEC endpoint. You receive delivery errors if you are using a self-signed certificate.

In this example, we only back up logs that fail during delivery.

To monitor your Firehose delivery stream, enable error logging. Doing this means that you can monitor record delivery errors.

Create an IAM role for the Firehose stream by choosing Create new, or Choose. Doing this brings you to a new screen. Choose Create a new IAM role, give the role a name, and then choose Allow.

If you look at the policy document, you can see that the role gives Kinesis Data Firehose permission to publish error logs to CloudWatch, execute your Lambda function, and put records into your S3 backup bucket.

You now get a chance to review and adjust the Firehose stream settings. When you are satisfied, choose Create Stream. You get a confirmation once the stream is created and active.

Create a VPC Flow Log

To send events from Amazon VPC, you need to set up a VPC flow log. If you already have a VPC flow log you want to use, you can skip to the “Publish CloudWatch to Kinesis Data Firehose” section.

On the AWS console, open the Amazon VPC service. Then choose VPC, Your VPC, and choose the VPC you want to send flow logs from. Choose Flow Logs, and then choose Create Flow Log. If you don’t have an IAM role that allows your VPC to publish logs to CloudWatch, choose Set Up Permissions and Create new role. Use the defaults when presented with the screen to create the new IAM role.

Once active, your VPC flow log should look like the following.

Publish CloudWatch to Kinesis Data Firehose

When you generate traffic to or from your VPC, the log group is created in Amazon CloudWatch. The new log group has no subscription filter, so set up a subscription filter. Setting this up establishes a real-time data feed from the log group to your Firehose delivery stream.

At present, you have to use the AWS Command Line Interface (AWS CLI) to create a CloudWatch Logs subscription to a Kinesis Data Firehose stream. However, you can use the AWS console to create subscriptions to Lambda and Amazon Elasticsearch Service.

To allow CloudWatch to publish to your Firehose stream, you need to give it permissions.

$ aws iam create-role --role-name CWLtoKinesisFirehoseRole --assume-role-policy-document file://TrustPolicyForCWLToFireHose.json

Here is the content for TrustPolicyForCWLToFireHose.json.

  "Statement": {
    "Effect": "Allow",
    "Principal": { "Service": "logs.us-east-1.amazonaws.com" },
    "Action": "sts:AssumeRole"


Attach the policy to the newly created role.

$ aws iam put-role-policy 
    --role-name CWLtoKinesisFirehoseRole 
    --policy-name Permissions-Policy-For-CWL 
    --policy-document file://PermissionPolicyForCWLToFireHose.json

Here is the content for PermissionPolicyForCWLToFireHose.json.

        "Resource":["arn:aws:firehose:us-east-1:YOUR-AWS-ACCT-NUM:deliverystream/ FirehoseSplunkDeliveryStream"]

Finally, create a subscription filter.

$ aws logs put-subscription-filter 
   --log-group-name " /vpc/flowlog/FirehoseSplunkDemo" 
   --filter-name "Destination" 
   --filter-pattern "" 
   --destination-arn "arn:aws:firehose:us-east-1:YOUR-AWS-ACCT-NUM:deliverystream/FirehoseSplunkDeliveryStream" 
   --role-arn "arn:aws:iam::YOUR-AWS-ACCT-NUM:role/CWLtoKinesisFirehoseRole"

When you run the AWS CLI command preceding, you don’t get any acknowledgment. To validate that your CloudWatch Log Group is subscribed to your Firehose stream, check the CloudWatch console.

As soon as the subscription filter is created, the real-time log data from the log group goes into your Firehose delivery stream. Your stream then delivers it to your Splunk Enterprise or Splunk Cloud environment for querying and visualization. The screenshot following is from Splunk Enterprise.

In addition, you can monitor and view metrics associated with your delivery stream using the AWS console.


Although our walkthrough uses VPC Flow Logs, the pattern can be used in many other scenarios. These include ingesting data from AWS IoT, other CloudWatch logs and events, Kinesis Streams or other data sources using the Kinesis Agent or Kinesis Producer Library. We also used Lambda blueprint Kinesis Data Firehose CloudWatch Logs Processor to transform streaming records from Kinesis Data Firehose. However, you might need to use a different Lambda blueprint or disable record transformation entirely depending on your use case. For an additional use case using Kinesis Data Firehose, check out This is My Architecture Video, which discusses how to securely centralize cross-account data analytics using Kinesis and Splunk.


Additional Reading

If you found this post useful, be sure to check out Integrating Splunk with Amazon Kinesis Streams and Using Amazon EMR and Hunk for Rapid Response Log Analysis and Review.

About the Authors

Tarik Makota is a solutions architect with the Amazon Web Services Partner Network. He provides technical guidance, design advice and thought leadership to AWS’ most strategic software partners. His career includes work in an extremely broad software development and architecture roles across ERP, financial printing, benefit delivery and administration and financial services. He holds an M.S. in Software Development and Management from Rochester Institute of Technology.




Roy Arsan is a solutions architect in the Splunk Partner Integrations team. He has a background in product development, cloud architecture, and building consumer and enterprise cloud applications. More recently, he has architected Splunk solutions on major cloud providers, including an AWS Quick Start for Splunk that enables AWS users to easily deploy distributed Splunk Enterprise straight from their AWS console. He’s also the co-author of the AWS Lambda blueprints for Splunk. He holds an M.S. in Computer Science Engineering from the University of Michigan.




Using Trusted Timestamping With Java

Post Syndicated from Bozho original https://techblog.bozho.net/using-trusted-timestamping-java/

Trusted timestamping is the process of having a trusted third party (“Time stamping authority”, TSA) certify the time of a given event in electronic form. The EU regulation eIDAS gives these timestamps legal strength – i.e. nobody can dispute the time or the content of the event if it was timestamped. It is applicable to multiple scenarios, including timestamping audit logs. (Note: timestamping is not sufficient for a good audit trail as it does not prevent a malicious actor from deleting the event altogether)

There are a number of standards for trusted timestamping, the core one being RFC 3161. As most RFCs it is hard to read. Fortunately for Java users, BouncyCastle implements the standard. Unfortunately, as with most security APIs, working with it is hard, even abysmal. I had to implement it, so I’ll share the code needed to timestamp data.

The whole gist can be found here, but I’ll try to explain the main flow. Obviously, there is a lot of code that’s there to simply follow the standard. The BouncyCastle classes are a maze that’s hard to navigate.

The main method is obviously timestamp(hash, tsaURL, username, password):

public TimestampResponseDto timestamp(byte[] hash, String tsaUrl, String tsaUsername, String tsaPassword) throws IOException {
    MessageImprint imprint = new MessageImprint(sha512oid, hash);

    TimeStampReq request = new TimeStampReq(imprint, null, new ASN1Integer(random.nextLong()),
            ASN1Boolean.TRUE, null);

    byte[] body = request.getEncoded();
    try {
        byte[] responseBytes = getTSAResponse(body, tsaUrl, tsaUsername, tsaPassword);

        ASN1StreamParser asn1Sp = new ASN1StreamParser(responseBytes);
        TimeStampResp tspResp = TimeStampResp.getInstance(asn1Sp.readObject());
        TimeStampResponse tsr = new TimeStampResponse(tspResp);

        checkForErrors(tsaUrl, tsr);

        // validate communication level attributes (RFC 3161 PKIStatus)
        tsr.validate(new TimeStampRequest(request));

        TimeStampToken token = tsr.getTimeStampToken();
        TimestampResponseDto response = new TimestampResponseDto();
        return response;
    } catch (RestClientException | TSPException | CMSException | OperatorCreationException | GeneralSecurityException e) {
        throw new IOException(e);

It prepares the request by creating the message imprint. Note that you are passing the hash itself, but also the hashing algorithm used to make the hash. Why isn’t the API hiding that from you, I don’t know. In my case the hash is obtained in a more complicated way, so it’s useful, but still. Then we get the raw form of the request and send it to the TSA (time stamping authority). It is an HTTP request, sort of simple, but you have to take care of some request and response headers that are not necessarily consistent across TSAs. The username and password are optional, some TSAs offer the service (rate-limited) without authentication.

When you have the raw response back, you parse it to a TimeStampResponse. Again, you have to go through 2 intermediate objects (ASN1StreamParser and TimeStampResp), which may be a proper abstraction, but is not a usable API.

Then you check if the response was successful, and you also have to validate it – the TSA may have returned a bad response. Ideally all of that could’ve been hidden from you. Validation throws an exception, which in this case I just propagate by wrapping in an IOException.

Finally, you get the token and return the response. The most important thing is the content of the token, which in my case was needed as Base64, so I encode it. It could just be the raw bytes as well. If you want to get any additional data from the token (e.g. the signing time), it’s not that simple; you have to parse the low-level attributes (seen in the gist).

Okay, you have the token now, and you can store it in a database. Occasionally you may want to validate whether timestamps have not been tampered with (which is my usecase). The code is here, and I won’t even try to explain it – it’s a ton of boilerplate that is also accounting for variations in the way TSAs respond (I’ve tried a few). The fact that a DummyCertificate class is needed either means I got something very wrong, or confirms my critique for the BouncyCastle APIs. The DummyCertificate may not be needed for some TSAs, but it is for others, and you actually can’t instantiate it that easily. You need a real certificate to construct it (which is not included in the gist; using the init() method in the next gist you can create the dummy with dummyCertificate = new DummyCertificate(certificateHolder.toASN1Structure());). In my code these are all one class, but for presenting them I decided to split it, hence this little duplication.

Okay, now we can timestamp and validate timestamps. That should be enough; but for testing purposes (or limited internal use) you may want to do the timestamping locally instead of asking a TSA. The code can be found here. It uses spring, but you can instead pass the keystore details as arguments to the init method. You need a JKS store with a keypair and a certificate, and I used KeyStore Explorer to create them. If you are running your application in AWS, you may want to encrypt your keystore using KMS (Key Management Service), and then decrypt it on application load, but that’s out of the scope of this article. For the local timestamping validation works as expected, and for timestamping – instead of calling the external service, just call localTSA.timestamp(req);

How did I get to know which classes to instantiate and which parameters to pass – I don’t remember. Looking at tests, examples, answers, sources. It took a while, and so I’m sharing it, to potentially save some trouble of others.

A list of TSAs you can test with: SafeCreative, FreeTSA, time.centum.pl.

I realize this does not seem applicable to many scenarios, but I would recommend timestamping some critical pieces of your application data. And it is generally useful to have it in your “toolbox”, ready to use, rather than trying to read the standard and battling with BouncyCastle classes for days in order to achieve this allegedly simple task.

The post Using Trusted Timestamping With Java appeared first on Bozho's tech blog.

The deal with Bitcoin

Post Syndicated from Michal Zalewski original http://lcamtuf.blogspot.com/2017/12/the-deal-with-bitcoin.html

♪ Used to have a little now I have a lot
I’m still, I’m still Jenny from the block
          chain ♪

For all that has been written about Bitcoin and its ilk, it is curious that the focus is almost solely what the cryptocurrencies are supposed to be. Technologists wax lyrical about the potential for blockchains to change almost every aspect of our lives. Libertarians and paleoconservatives ache for the return to “sound money” that can’t be conjured up at the whim of a bureaucrat. Mainstream economists wag their fingers, proclaiming that a proper currency can’t be deflationary, that it must maintain a particular velocity, or that the government must be able to nip crises of confidence in the bud. And so on.

Much of this may be true, but the proponents of cryptocurrencies should recognize that an appeal to consequences is not a guarantee of good results. The critics, on the other hand, would be best served to remember that they are drawing far-reaching conclusions about the effects of modern monetary policies based on a very short and tumultuous period in history.

In this post, my goal is to ditch most of the dogma, talk a bit about the origins of money – and then see how “crypto” fits the bill.

1. The prehistory of currencies

The emergence of money is usually explained in a very straightforward way. You know the story: a farmer raised a pig, a cobbler made a shoe. The cobbler needed to feed his family while the farmer wanted to keep his feet warm – and so they met to exchange the goods on mutually beneficial terms. But as the tale goes, the barter system had a fatal flaw: sometimes, a farmer wanted a cooking pot, a potter wanted a knife, and a blacksmith wanted a pair of pants. To facilitate increasingly complex, multi-step exchanges without requiring dozens of people to meet face to face, we came up with an abstract way to represent value – a shiny coin guaranteed to be accepted by every tradesman.

It is a nice parable, but it probably isn’t very true. It seems far more plausible that early societies relied on the concept of debt long before the advent of currencies: an informal tally or a formal ledger would be used to keep track of who owes what to whom. The concept of debt, closely associated with one’s trustworthiness and standing in the community, would have enabled a wide range of economic activities: debts could be paid back over time, transferred, renegotiated, or forgotten – all without having to engage in spot barter or to mint a single coin. In fact, such non-monetary, trust-based, reciprocal economies are still common in closely-knit communities: among families, neighbors, coworkers, or friends.

In such a setting, primitive currencies probably emerged simply as a consequence of having a system of prices: a cow being worth a particular number of chickens, a chicken being worth a particular number of beaver pelts, and so forth. Formalizing such relationships by settling on a single, widely-known unit of account – say, one chicken – would make it more convenient to transfer, combine, or split debts; or to settle them in alternative goods.

Contrary to popular belief, for communal ledgers, the unit of account probably did not have to be particularly desirable, durable, or easy to carry; it was simply an accounting tool. And indeed, we sometimes run into fairly unusual units of account even in modern times: for example, cigarettes can be the basis of a bustling prison economy even when most inmates don’t smoke and there are not that many packs to go around.

2. The age of commodity money

In the end, the development of coinage might have had relatively little to do with communal trade – and far more with the desire to exchange goods with strangers. When dealing with a unfamiliar or hostile tribe, the concept of a chicken-denominated ledger does not hold up: the other side might be disinclined to honor its obligations – and get away with it, too. To settle such problematic trades, we needed a “spot” medium of exchange that would be easy to carry and authenticate, had a well-defined value, and a near-universal appeal. Throughout much of the recorded history, precious metals – predominantly gold and silver – proved to fit the bill.

In the most basic sense, such commodities could be seen as a tool to reconcile debts across societal boundaries, without necessarily replacing any local units of account. An obligation, denominated in some local currency, would be created on buyer’s side in order to procure the metal for the trade. The proceeds of the completed transaction would in turn allow the seller to settle their own local obligations that arose from having to source the traded goods. In other words, our wondrous chicken-denominated ledgers could coexist peacefully with gold – and when commodity coinage finally took hold, it’s likely that in everyday trade, precious metals served more as a useful abstraction than a precise store of value. A “silver chicken” of sorts.

Still, the emergence of commodity money had one interesting side effect: it decoupled the unit of debt – a “claim on the society”, in a sense – from any moral judgment about its origin. A piece of silver would buy the same amount of food, whether earned through hard labor or won in a drunken bet. This disconnect remains a central theme in many of the debates about social justice and unfairly earned wealth.

3. The State enters the game

If there is one advantage of chicken ledgers over precious metals, it’s that all chickens look and cluck roughly the same – something that can’t be said of every nugget of silver or gold. To cope with this problem, we needed to shape raw commodities into pieces of a more predictable shape and weight; a trusted party could then stamp them with a mark to indicate the value and the quality of the coin.

At first, the task of standardizing coinage rested with private parties – but the responsibility was soon assumed by the State. The advantages of this transition seemed clear: a single, widely-accepted and easily-recognizable currency could be now used to settle virtually all private and official debts.

Alas, in what deserves the dubious distinction of being one of the earliest examples of monetary tomfoolery, some States succumbed to the temptation of fiddling with the coinage to accomplish anything from feeding the poor to waging wars. In particular, it would be common to stamp coins with the same face value but a progressively lower content of silver and gold. Perhaps surprisingly, the strategy worked remarkably well; at least in the times of peace, most people cared about the value stamped on the coin, not its precise composition or weight.

And so, over time, representative money was born: sooner or later, most States opted to mint coins from nearly-worthless metals, or print banknotes on paper and cloth. This radically new currency was accompanied with a simple pledge: the State offered to redeem it at any time for its nominal value in gold.

Of course, the promise was largely illusory: the State did not have enough gold to honor all the promises it had made. Still, as long as people had faith in their rulers and the redemption requests stayed low, the fundamental mechanics of this new representative currency remained roughly the same as before – and in some ways, were an improvement in that they lessened the insatiable demand for a rare commodity. Just as importantly, the new money still enabled international trade – using the underlying gold exchange rate as a reference point.

4. Fractional reserve banking and fiat money

For much of the recorded history, banking was an exceptionally dull affair, not much different from running a communal chicken
ledger of the old. But then, something truly marvelous happened in the 17th century: around that time, many European countries have witnessed
the emergence of fractional-reserve banks.

These private ventures operated according to a simple scheme: they accepted people’s coin
for safekeeping, promising to pay a premium on every deposit made. To meet these obligations and to make a profit, the banks then
used the pooled deposits to make high-interest loans to other folks. The financiers figured out that under normal circumstances
and when operating at a sufficient scale, they needed only a very modest reserve – well under 10% of all deposited money – to be
able to service the usual volume and size of withdrawals requested by their customers. The rest could be loaned out.

The very curious consequence of fractional-reserve banking was that it pulled new money out of thin air.
The funds were simultaneously accounted for in the statements shown to the depositor, evidently available for withdrawal or
transfer at any time; and given to third-party borrowers, who could spend them on just about anything. Heck, the borrowers could
deposit the proceeds in another bank, creating even more money along the way! Whatever they did, the sum of all funds in the monetary
system now appeared much higher than the value of all coins and banknotes issued by the government – let alone the amount of gold
sitting in any vault.

Of course, no new money was being created in any physical sense: all that banks were doing was engaging in a bit of creative accounting – the sort of which would probably land you in jail if you attempted it today in any other comparably vital field of enterprise. If too many depositors were to ask for their money back, or if too many loans were to go bad, the banking system would fold. Fortunes would evaporate in a puff of accounting smoke, and with the disappearance of vast quantities of quasi-fictitious (“broad”) money, the wealth of the entire nation would shrink.

In the early 20th century, the world kept witnessing just that; a series of bank runs and economic contractions forced the governments around the globe to act. At that stage, outlawing fractional-reserve banking was no longer politically or economically tenable; a simpler alternative was to let go of gold and move to fiat money – a currency implemented as an abstract social construct, with no predefined connection to the physical realm. A new breed of economists saw the role of the government not in trying to peg the value of money to an inflexible commodity, but in manipulating its supply to smooth out economic hiccups or to stimulate growth.

(Contrary to popular beliefs, such manipulation is usually not done by printing new banknotes; more sophisticated methods, such as lowering reserve requirements for bank deposits or enticing banks to invest its deposits into government-issued securities, are the preferred route.)

The obvious peril of fiat money is that in the long haul, its value is determined strictly by people’s willingness to accept a piece of paper in exchange for their trouble; that willingness, in turn, is conditioned solely on their belief that the same piece of paper would buy them something nice a week, a month, or a year from now. It follows that a simple crisis of confidence could make a currency nearly worthless overnight. A prolonged period of hyperinflation and subsequent austerity in Germany and Austria was one of the precipitating factors that led to World War II. In more recent times, dramatic episodes of hyperinflation plagued the fiat currencies of Israel (1984), Mexico (1988), Poland (1990), Yugoslavia (1994), Bulgaria (1996), Turkey (2002), Zimbabwe (2009), Venezuela (2016), and several other nations around the globe.

For the United States, the switch to fiat money came relatively late, in 1971. To stop the dollar from plunging like a rock, the Nixon administration employed a clever trick: they ordered the freeze of wages and prices for the 90 days that immediately followed the move. People went on about their lives and paid the usual for eggs or milk – and by the time the freeze ended, they were accustomed to the idea that the “new”, free-floating dollar is worth about the same as the old, gold-backed one. A robust economy and favorable geopolitics did the rest, and so far, the American adventure with fiat currency has been rather uneventful – perhaps except for the fact that the price of gold itself skyrocketed from $35 per troy ounce in 1971 to $850 in 1980 (or, from $210 to $2,500 in today’s dollars).

Well, one thing did change: now better positioned to freely tamper with the supply of money, the regulators in accord with the bankers adopted a policy of creating it at a rate that slightly outstripped the organic growth in economic activity. They did this to induce a small, steady degree of inflation, believing that doing so would discourage people from hoarding cash and force them to reinvest it for the betterment of the society. Some critics like to point out that such a policy functions as a “backdoor” tax on savings that happens to align with the regulators’ less noble interests; still, either way: in the US and most other developed nations, the purchasing power of any money kept under a mattress will drop at a rate of somewhere between 2 to 10% a year.

5. So what’s up with Bitcoin?

Well… countless tomes have been written about the nature and the optimal characteristics of government-issued fiat currencies. Some heterodox economists, notably including Murray Rothbard, have also explored the topic of privately-issued, decentralized, commodity-backed currencies. But Bitcoin is a wholly different animal.

In essence, BTC is a global, decentralized fiat currency: it has no (recoverable) intrinsic value, no central authority to issue it or define its exchange rate, and it has no anchoring to any historical reference point – a combination that until recently seemed nonsensical and escaped any serious scrutiny. It does the unthinkable by employing three clever tricks:

  1. It allows anyone to create new coins, but only by solving brute-force computational challenges that get more difficult as the time goes by,

  2. It prevents unauthorized transfer of coins by employing public key cryptography to sign off transactions, with only the authorized holder of a coin knowing the correct key,

  3. It prevents double-spending by using a distributed public ledger (“blockchain”), recording the chain of custody for coins in a tamper-proof way.

The blockchain is often described as the most important feature of Bitcoin, but in some ways, its importance is overstated. The idea of a currency that does not rely on a centralized transaction clearinghouse is what helped propel the platform into the limelight – mostly because of its novelty and the perception that it is less vulnerable to government meddling (although the government is still free to track down, tax, fine, or arrest any participants). On the flip side, the everyday mechanics of BTC would not be fundamentally different if all the transactions had to go through Bitcoin Bank, LLC.

A more striking feature of the new currency is the incentive structure surrounding the creation of new coins. The underlying design democratized the creation of new coins early on: all you had to do is leave your computer running for a while to acquire a number of tokens. The tokens had no practical value, but obtaining them involved no substantial expense or risk. Just as importantly, because the difficulty of the puzzles would only increase over time, the hope was that if Bitcoin caught on, latecomers would find it easier to purchase BTC on a secondary market than mine their own – paying with a more established currency at a mutually beneficial exchange rate.

The persistent publicity surrounding Bitcoin and other cryptocurrencies did the rest – and today, with the growing scarcity of coins and the rapidly increasing demand, the price of a single token hovers somewhere south of $15,000.

6. So… is it bad money?

Predicting is hard – especially the future. In some sense, a coin that represents a cryptographic proof of wasted CPU cycles is no better or worse than a currency that relies on cotton decorated with pictures of dead presidents. It is true that Bitcoin suffers from many implementation problems – long transaction processing times, high fees, frequent security breaches of major exchanges – but in principle, such problems can be overcome.

That said, currencies live and die by the lasting willingness of others to accept them in exchange for services or goods – and in that sense, the jury is still out. The use of Bitcoin to settle bona fide purchases is negligible, both in absolute terms and in function of the overall volume of transactions. In fact, because of the technical challenges and limited practical utility, some companies that embraced the currency early on are now backing out.

When the value of an asset is derived almost entirely from its appeal as an ever-appreciating investment vehicle, the situation has all the telltale signs of a speculative bubble. But that does not prove that the asset is destined to collapse, or that a collapse would be its end. Still, the built-in deflationary mechanism of Bitcoin – the increasing difficulty of producing new coins – is probably both a blessing and a curse.

It’s going to go one way or the other; and when it’s all said and done, we’re going to celebrate the people who made the right guess. Because future is actually pretty darn easy to predict — in retrospect.

Managing AWS Lambda Function Concurrency

Post Syndicated from Chris Munns original https://aws.amazon.com/blogs/compute/managing-aws-lambda-function-concurrency/

One of the key benefits of serverless applications is the ease in which they can scale to meet traffic demands or requests, with little to no need for capacity planning. In AWS Lambda, which is the core of the serverless platform at AWS, the unit of scale is a concurrent execution. This refers to the number of executions of your function code that are happening at any given time.

Thinking about concurrent executions as a unit of scale is a fairly unique concept. In this post, I dive deeper into this and talk about how you can make use of per function concurrency limits in Lambda.

Understanding concurrency in Lambda

Instead of diving right into the guts of how Lambda works, here’s an appetizing analogy: a magical pizza.
Yes, a magical pizza!

This magical pizza has some unique properties:

  • It has a fixed maximum number of slices, such as 8.
  • Slices automatically re-appear after they are consumed.
  • When you take a slice from the pizza, it does not re-appear until it has been completely consumed.
  • One person can take multiple slices at a time.
  • You can easily ask to have the number of slices increased, but they remain fixed at any point in time otherwise.

Now that the magical pizza’s properties are defined, here’s a hypothetical situation of some friends sharing this pizza.

Shawn, Kate, Daniela, Chuck, Ian and Avleen get together every Friday to share a pizza and catch up on their week. As there is just six of them, they can easily all enjoy a slice of pizza at a time. As they finish each slice, it re-appears in the pizza pan and they can take another slice again. Given the magical properties of their pizza, they can continue to eat all they want, but with two very important constraints:

  • If any of them take too many slices at once, the others may not get as much as they want.
  • If they take too many slices, they might also eat too much and get sick.

One particular week, some of the friends are hungrier than the rest, taking two slices at a time instead of just one. If more than two of them try to take two pieces at a time, this can cause contention for pizza slices. Some of them would wait hungry for the slices to re-appear. They could ask for a pizza with more slices, but then run the same risk again later if more hungry friends join than planned for.

What can they do?

If the friends agreed to accept a limit for the maximum number of slices they each eat concurrently, both of these issues are avoided. Some could have a maximum of 2 of the 8 slices, or other concurrency limits that were more or less. Just so long as they kept it at or under eight total slices to be eaten at one time. This would keep any from going hungry or eating too much. The six friends can happily enjoy their magical pizza without worry!

Concurrency in Lambda

Concurrency in Lambda actually works similarly to the magical pizza model. Each AWS Account has an overall AccountLimit value that is fixed at any point in time, but can be easily increased as needed, just like the count of slices in the pizza. As of May 2017, the default limit is 1000 “slices” of concurrency per AWS Region.

Also like the magical pizza, each concurrency “slice” can only be consumed individually one at a time. After consumption, it becomes available to be consumed again. Services invoking Lambda functions can consume multiple slices of concurrency at the same time, just like the group of friends can take multiple slices of the pizza.

Let’s take our example of the six friends and bring it back to AWS services that commonly invoke Lambda:

  • Amazon S3
  • Amazon Kinesis
  • Amazon DynamoDB
  • Amazon Cognito

In a single account with the default concurrency limit of 1000 concurrent executions, any of these four services could invoke enough functions to consume the entire limit or some part of it. Just like with the pizza example, there is the possibility for two issues to pop up:

  • One or more of these services could invoke enough functions to consume a majority of the available concurrency capacity. This could cause others to be starved for it, causing failed invocations.
  • A service could consume too much concurrent capacity and cause a downstream service or database to be overwhelmed, which could cause failed executions.

For Lambda functions that are launched in a VPC, you have the potential to consume the available IP addresses in a subnet or the maximum number of elastic network interfaces to which your account has access. For more information, see Configuring a Lambda Function to Access Resources in an Amazon VPC. For information about elastic network interface limits, see Network Interfaces section in the Amazon VPC Limits topic.

One way to solve both of these problems is applying a concurrency limit to the Lambda functions in an account.

Configuring per function concurrency limits

You can now set a concurrency limit on individual Lambda functions in an account. The concurrency limit that you set reserves a portion of your account level concurrency for a given function. All of your functions’ concurrent executions count against this account-level limit by default.

If you set a concurrency limit for a specific function, then that function’s concurrency limit allocation is deducted from the shared pool and assigned to that specific function. AWS also reserves 100 units of concurrency for all functions that don’t have a specified concurrency limit set. This helps to make sure that future functions have capacity to be consumed.

Going back to the example of the consuming services, you could set throttles for the functions as follows:

Amazon S3 function = 350
Amazon Kinesis function = 200
Amazon DynamoDB function = 200
Amazon Cognito function = 150
Total = 900

With the 100 reserved for all non-concurrency reserved functions, this totals the account limit of 1000.

Here’s how this works. To start, create a basic Lambda function that is invoked via Amazon API Gateway. This Lambda function returns a single “Hello World” statement with an added sleep time between 2 and 5 seconds. The sleep time simulates an API providing some sort of capability that can take a varied amount of time. The goal here is to show how an API that is underloaded can reach its concurrency limit, and what happens when it does.
To create the example function

  1. Open the Lambda console.
  2. Choose Create Function.
  3. For Author from scratch, enter the following values:
    1. For Name, enter a value (such as concurrencyBlog01).
    2. For Runtime, choose Python 3.6.
    3. For Role, choose Create new role from template and enter a name aligned with this function, such as concurrencyBlogRole.
  4. Choose Create function.
  5. The function is created with some basic example code. Replace that code with the following:

import time
from random import randint
seconds = randint(2, 5)

def lambda_handler(event, context):
return {"statusCode": 200,
"body": ("Hello world, slept " + str(seconds) + " seconds"),
"Access-Control-Allow-Headers": "Content-Type,X-Amz-Date,Authorization,X-Api-Key,X-Amz-Security-Token",
"Access-Control-Allow-Methods": "GET,OPTIONS",

  1. Under Basic settings, set Timeout to 10 seconds. While this function should only ever take up to 5-6 seconds (with the 5-second max sleep), this gives you a little bit of room if it takes longer.

  1. Choose Save at the top right.

At this point, your function is configured for this example. Test it and confirm this in the console:

  1. Choose Test.
  2. Enter a name (it doesn’t matter for this example).
  3. Choose Create.
  4. In the console, choose Test again.
  5. You should see output similar to the following:

Now configure API Gateway so that you have an HTTPS endpoint to test against.

  1. In the Lambda console, choose Configuration.
  2. Under Triggers, choose API Gateway.
  3. Open the API Gateway icon now shown as attached to your Lambda function:

  1. Under Configure triggers, leave the default values for API Name and Deployment stage. For Security, choose Open.
  2. Choose Add, Save.

API Gateway is now configured to invoke Lambda at the Invoke URL shown under its configuration. You can take this URL and test it in any browser or command line, using tools such as “curl”:

$ curl https://ofixul557l.execute-api.us-east-1.amazonaws.com/prod/concurrencyBlog01
Hello world, slept 2 seconds

Throwing load at the function

Now start throwing some load against your API Gateway + Lambda function combo. Right now, your function is only limited by the total amount of concurrency available in an account. For this example account, you might have 850 unreserved concurrency out of a full account limit of 1000 due to having configured a few concurrency limits already (also the 100 concurrency saved for all functions without configured limits). You can find all of this information on the main Dashboard page of the Lambda console:

For generating load in this example, use an open source tool called “hey” (https://github.com/rakyll/hey), which works similarly to ApacheBench (ab). You test from an Amazon EC2 instance running the default Amazon Linux AMI from the EC2 console. For more help with configuring an EC2 instance, follow the steps in the Launch Instance Wizard.

After the EC2 instance is running, SSH into the host and run the following:

sudo yum install go
go get -u github.com/rakyll/hey

“hey” is easy to use. For these tests, specify a total number of tests (5,000) and a concurrency of 50 against the API Gateway URL as follows(replace the URL here with your own):

$ ./go/bin/hey -n 5000 -c 50 https://ofixul557l.execute-api.us-east-1.amazonaws.com/prod/concurrencyBlog01

The output from “hey” tells you interesting bits of information:

$ ./go/bin/hey -n 5000 -c 50 https://ofixul557l.execute-api.us-east-1.amazonaws.com/prod/concurrencyBlog01

Total: 381.9978 secs
Slowest: 9.4765 secs
Fastest: 0.0438 secs
Average: 3.2153 secs
Requests/sec: 13.0891
Total data: 140024 bytes
Size/request: 28 bytes

Response time histogram:
0.044 [1] |
0.987 [2] |
1.930 [0] |
2.874 [1803] |∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
3.817 [1518] |∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
4.760 [719] |∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
5.703 [917] |∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
6.647 [13] |
7.590 [14] |
8.533 [9] |
9.477 [4] |

Latency distribution:
10% in 2.0224 secs
25% in 2.0267 secs
50% in 3.0251 secs
75% in 4.0269 secs
90% in 5.0279 secs
95% in 5.0414 secs
99% in 5.1871 secs

Details (average, fastest, slowest):
DNS+dialup: 0.0003 secs, 0.0000 secs, 0.0332 secs
DNS-lookup: 0.0000 secs, 0.0000 secs, 0.0046 secs
req write: 0.0000 secs, 0.0000 secs, 0.0005 secs
resp wait: 3.2149 secs, 0.0438 secs, 9.4472 secs
resp read: 0.0000 secs, 0.0000 secs, 0.0004 secs

Status code distribution:
[200] 4997 responses
[502] 3 responses

You can see a helpful histogram and latency distribution. Remember that this Lambda function has a random sleep period in it and so isn’t entirely representational of a real-life workload. Those three 502s warrant digging deeper, but could be due to Lambda cold-start timing and the “second” variable being the maximum of 5, causing the Lambda functions to time out. AWS X-Ray and the Amazon CloudWatch logs generated by both API Gateway and Lambda could help you troubleshoot this.

Configuring a concurrency reservation

Now that you’ve established that you can generate this load against the function, I show you how to limit it and protect a backend resource from being overloaded by all of these requests.

  1. In the console, choose Configure.
  2. Under Concurrency, for Reserve concurrency, enter 25.

  1. Click on Save in the top right corner.

You could also set this with the AWS CLI using the Lambda put-function-concurrency command or see your current concurrency configuration via Lambda get-function. Here’s an example command:

$ aws lambda get-function --function-name concurrencyBlog01 --output json --query Concurrency
"ReservedConcurrentExecutions": 25

Either way, you’ve set the Concurrency Reservation to 25 for this function. This acts as both a limit and a reservation in terms of making sure that you can execute 25 concurrent functions at all times. Going above this results in the throttling of the Lambda function. Depending on the invoking service, throttling can result in a number of different outcomes, as shown in the documentation on Throttling Behavior. This change has also reduced your unreserved account concurrency for other functions by 25.

Rerun the same load generation as before and see what happens. Previously, you tested at 50 concurrency, which worked just fine. By limiting the Lambda functions to 25 concurrency, you should see rate limiting kick in. Run the same test again:

$ ./go/bin/hey -n 5000 -c 50 https://ofixul557l.execute-api.us-east-1.amazonaws.com/prod/concurrencyBlog01

While this test runs, refresh the Monitoring tab on your function detail page. You see the following warning message:

This is great! It means that your throttle is working as configured and you are now protecting your downstream resources from too much load from your Lambda function.

Here is the output from a new “hey” command:

$ ./go/bin/hey -n 5000 -c 50 https://ofixul557l.execute-api.us-east-1.amazonaws.com/prod/concurrencyBlog01
Total: 379.9922 secs
Slowest: 7.1486 secs
Fastest: 0.0102 secs
Average: 1.1897 secs
Requests/sec: 13.1582
Total data: 164608 bytes
Size/request: 32 bytes

Response time histogram:
0.010 [1] |
0.724 [3075] |∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎∎
1.438 [0] |
2.152 [811] |∎∎∎∎∎∎∎∎∎∎∎
2.866 [11] |
3.579 [566] |∎∎∎∎∎∎∎
4.293 [214] |∎∎∎
5.007 [1] |
5.721 [315] |∎∎∎∎
6.435 [4] |
7.149 [2] |

Latency distribution:
10% in 0.0130 secs
25% in 0.0147 secs
50% in 0.0205 secs
75% in 2.0344 secs
90% in 4.0229 secs
95% in 5.0248 secs
99% in 5.0629 secs

Details (average, fastest, slowest):
DNS+dialup: 0.0004 secs, 0.0000 secs, 0.0537 secs
DNS-lookup: 0.0002 secs, 0.0000 secs, 0.0184 secs
req write: 0.0000 secs, 0.0000 secs, 0.0016 secs
resp wait: 1.1892 secs, 0.0101 secs, 7.1038 secs
resp read: 0.0000 secs, 0.0000 secs, 0.0005 secs

Status code distribution:
[502] 3076 responses
[200] 1924 responses

This looks fairly different from the last load test run. A large percentage of these requests failed fast due to the concurrency throttle failing them (those with the 0.724 seconds line). The timing shown here in the histogram represents the entire time it took to get a response between the EC2 instance and API Gateway calling Lambda and being rejected. It’s also important to note that this example was configured with an edge-optimized endpoint in API Gateway. You see under Status code distribution that 3076 of the 5000 requests failed with a 502, showing that the backend service from API Gateway and Lambda failed the request.

Other uses

Managing function concurrency can be useful in a few other ways beyond just limiting the impact on downstream services and providing a reservation of concurrency capacity. Here are two other uses:

  • Emergency kill switch
  • Cost controls

Emergency kill switch

On occasion, due to issues with applications I’ve managed in the past, I’ve had a need to disable a certain function or capability of an application. By setting the concurrency reservation and limit of a Lambda function to zero, you can do just that.

With the reservation set to zero every invocation of a Lambda function results in being throttled. You could then work on the related parts of the infrastructure or application that aren’t working, and then reconfigure the concurrency limit to allow invocations again.

Cost controls

While I mentioned how you might want to use concurrency limits to control the downstream impact to services or databases that your Lambda function might call, another resource that you might be cautious about is money. Setting the concurrency throttle is another way to help control costs during development and testing of your application.

You might want to prevent against a function performing a recursive action too quickly or a development workload generating too high of a concurrency. You might also want to protect development resources connected to this function from generating too much cost, such as APIs that your Lambda function calls.


Concurrent executions as a unit of scale are a fairly unique characteristic about Lambda functions. Placing limits on how many concurrency “slices” that your function can consume can prevent a single function from consuming all of the available concurrency in an account. Limits can also prevent a function from overwhelming a backend resource that isn’t as scalable.

Unlike monolithic applications or even microservices where there are mixed capabilities in a single service, Lambda functions encourage a sort of “nano-service” of small business logic directly related to the integration model connected to the function. I hope you’ve enjoyed this post and configure your concurrency limits today!

Potential impact of the Intel ME vulnerability

Post Syndicated from Matthew Garrett original https://mjg59.dreamwidth.org/49611.html

(Note: this is my personal opinion based on public knowledge around this issue. I have no knowledge of any non-public details of these vulnerabilities, and this should not be interpreted as the position or opinion of my employer)

Intel’s Management Engine (ME) is a small coprocessor built into the majority of Intel CPUs[0]. Older versions were based on the ARC architecture[1] running an embedded realtime operating system, but from version 11 onwards they’ve been small x86 cores running Minix. The precise capabilities of the ME have not been publicly disclosed, but it is at minimum capable of interacting with the network[2], display[3], USB, input devices and system flash. In other words, software running on the ME is capable of doing a lot, without requiring any OS permission in the process.

Back in May, Intel announced a vulnerability in the Advanced Management Technology (AMT) that runs on the ME. AMT offers functionality like providing a remote console to the system (so IT support can connect to your system and interact with it as if they were physically present), remote disk support (so IT support can reinstall your machine over the network) and various other bits of system management. The vulnerability meant that it was possible to log into systems with enabled AMT with an empty authentication token, making it possible to log in without knowing the configured password.

This vulnerability was less serious than it could have been for a couple of reasons – the first is that “consumer”[4] systems don’t ship with AMT, and the second is that AMT is almost always disabled (Shodan found only a few thousand systems on the public internet with AMT enabled, out of many millions of laptops). I wrote more about it here at the time.

How does this compare to the newly announced vulnerabilities? Good question. Two of the announced vulnerabilities are in AMT. The previous AMT vulnerability allowed you to bypass authentication, but restricted you to doing what AMT was designed to let you do. While AMT gives an authenticated user a great deal of power, it’s also designed with some degree of privacy protection in mind – for instance, when the remote console is enabled, an animated warning border is drawn on the user’s screen to alert them.

This vulnerability is different in that it allows an authenticated attacker to execute arbitrary code within the AMT process. This means that the attacker shouldn’t have any capabilities that AMT doesn’t, but it’s unclear where various aspects of the privacy protection are implemented – for instance, if the warning border is implemented in AMT rather than in hardware, an attacker could duplicate that functionality without drawing the warning. If the USB storage emulation for remote booting is implemented as a generic USB passthrough, the attacker could pretend to be an arbitrary USB device and potentially exploit the operating system through bugs in USB device drivers. Unfortunately we don’t currently know.

Note that this exploit still requires two things – first, AMT has to be enabled, and second, the attacker has to be able to log into AMT. If the attacker has physical access to your system and you don’t have a BIOS password set, they will be able to enable it – however, if AMT isn’t enabled and the attacker isn’t physically present, you’re probably safe. But if AMT is enabled and you haven’t patched the previous vulnerability, the attacker will be able to access AMT over the network without a password and then proceed with the exploit. This is bad, so you should probably (1) ensure that you’ve updated your BIOS and (2) ensure that AMT is disabled unless you have a really good reason to use it.

The AMT vulnerability applies to a wide range of versions, everything from version 6 (which shipped around 2008) and later. The other vulnerability that Intel describe is restricted to version 11 of the ME, which only applies to much more recent systems. This vulnerability allows an attacker to execute arbitrary code on the ME, which means they can do literally anything the ME is able to do. This probably also means that they are able to interfere with any other code running on the ME. While AMT has been the most frequently discussed part of this, various other Intel technologies are tied to ME functionality.

Intel’s Platform Trust Technology (PTT) is a software implementation of a Trusted Platform Module (TPM) that runs on the ME. TPMs are intended to protect access to secrets and encryption keys and record the state of the system as it boots, making it possible to determine whether a system has had part of its boot process modified and denying access to the secrets as a result. The most common usage of TPMs is to protect disk encryption keys – Microsoft Bitlocker defaults to storing its encryption key in the TPM, automatically unlocking the drive if the boot process is unmodified. In addition, TPMs support something called Remote Attestation (I wrote about that here), which allows the TPM to provide a signed copy of information about what the system booted to a remote site. This can be used for various purposes, such as not allowing a compute node to join a cloud unless it’s booted the correct version of the OS and is running the latest firmware version. Remote Attestation depends on the TPM having a unique cryptographic identity that is tied to the TPM and inaccessible to the OS.

PTT allows manufacturers to simply license some additional code from Intel and run it on the ME rather than having to pay for an additional chip on the system motherboard. This seems great, but if an attacker is able to run code on the ME then they potentially have the ability to tamper with PTT, which means they can obtain access to disk encryption secrets and circumvent Bitlocker. It also means that they can tamper with Remote Attestation, “attesting” that the system booted a set of software that it didn’t or copying the keys to another system and allowing that to impersonate the first. This is, uh, bad.

Intel also recently announced Intel Online Connect, a mechanism for providing the functionality of security keys directly in the operating system. Components of this are run on the ME in order to avoid scenarios where a compromised OS could be used to steal the identity secrets – if the ME is compromised, this may make it possible for an attacker to obtain those secrets and duplicate the keys.

It’s also not entirely clear how much of Intel’s Secure Guard Extensions (SGX) functionality depends on the ME. The ME does appear to be required for SGX Remote Attestation (which allows an application using SGX to prove to a remote site that it’s the SGX app rather than something pretending to be it), and again if those secrets can be extracted from a compromised ME it may be possible to compromise some of the security assumptions around SGX. Again, it’s not clear how serious this is because it’s not publicly documented.

Various other things also run on the ME, including stuff like video DRM (ensuring that high resolution video streams can’t be intercepted by the OS). It may be possible to obtain encryption keys from a compromised ME that allow things like Netflix streams to be decoded and dumped. From a user privacy or security perspective, these things seem less serious.

The big problem at the moment is that we have no idea what the actual process of compromise is. Intel state that it requires local access, but don’t describe what kind. Local access in this case could simply require the ability to send commands to the ME (possible on any system that has the ME drivers installed), could require direct hardware access to the exposed ME (which would require either kernel access or the ability to install a custom driver) or even the ability to modify system flash (possible only if the attacker has physical access and enough time and skill to take the system apart and modify the flash contents with an SPI programmer). The other thing we don’t know is whether it’s possible for an attacker to modify the system such that the ME is persistently compromised or whether it needs to be re-compromised every time the ME reboots. Note that even the latter is more serious than you might think – the ME may only be rebooted if the system loses power completely, so even a “temporary” compromise could affect a system for a long period of time.

It’s also almost impossible to determine if a system is compromised. If the ME is compromised then it’s probably possible for it to roll back any firmware updates but still report that it’s been updated, giving admins a false sense of security. The only way to determine for sure would be to dump the system flash and compare it to a known good image. This is impractical to do at scale.

So, overall, given what we know right now it’s hard to say how serious this is in terms of real world impact. It’s unlikely that this is the kind of vulnerability that would be used to attack individual end users – anyone able to compromise a system like this could just backdoor your browser instead with much less effort, and that already gives them your banking details. The people who have the most to worry about here are potential targets of skilled attackers, which means activists, dissidents and companies with interesting personal or business data. It’s hard to make strong recommendations about what to do here without more insight into what the vulnerability actually is, and we may not know that until this presentation next month.

Summary: Worst case here is terrible, but unlikely to be relevant to the vast majority of users.

[0] Earlier versions of the ME were built into the motherboard chipset, but as portions of that were incorporated onto the CPU package the ME followed
[1] A descendent of the SuperFX chip used in Super Nintendo cartridges such as Starfox, because why not
[2] Without any OS involvement for wired ethernet and for wireless networks in the system firmware, but requires OS support for wireless access once the OS drivers have loaded
[3] Assuming you’re using integrated Intel graphics
[4] “Consumer” is a bit of a misnomer here – “enterprise” laptops like Thinkpads ship with AMT, but are often bought by consumers.

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The 10 Most Viewed Security-Related AWS Knowledge Center Articles and Videos for November 2017

Post Syndicated from Maggie Burke original https://aws.amazon.com/blogs/security/the-10-most-viewed-security-related-aws-knowledge-center-articles-and-videos-for-november-2017/

AWS Knowledge Center image

The AWS Knowledge Center helps answer the questions most frequently asked by AWS Support customers. The following 10 Knowledge Center security articles and videos have been the most viewed this month. It’s likely you’ve wondered about a few of these topics yourself, so here’s a chance to learn the answers!

  1. How do I create an AWS Identity and Access Management (IAM) policy to restrict access for an IAM user, group, or role to a particular Amazon Virtual Private Cloud (VPC)?
    Learn how to apply a custom IAM policy to restrict IAM user, group, or role permissions for creating and managing Amazon EC2 instances in a specified VPC.
  2. How do I use an MFA token to authenticate access to my AWS resources through the AWS CLI?
    One IAM best practice is to protect your account and its resources by using a multi-factor authentication (MFA) device. If you plan use the AWS Command Line Interface (CLI) while using an MFA device, you must create a temporary session token.
  3. Can I restrict an IAM user’s EC2 access to specific resources?
    This article demonstrates how to link multiple AWS accounts through AWS Organizations and isolate IAM user groups in their own accounts.
  4. I didn’t receive a validation email for the SSL certificate I requested through AWS Certificate Manager (ACM)—where is it?
    Can’t find your ACM validation emails? Be sure to check the email address to which you requested that ACM send validation emails.
  5. How do I create an IAM policy that has a source IP restriction but still allows users to switch roles in the AWS Management Console?
    Learn how to write an IAM policy that not only includes a source IP restriction but also lets your users switch roles in the console.
  6. How do I allow users from another account to access resources in my account through IAM?
    If you have the 12-digit account number and permissions to create and edit IAM roles and users for both accounts, you can permit specific IAM users to access resources in your account.
  7. What are the differences between a service control policy (SCP) and an IAM policy?
    Learn how to distinguish an SCP from an IAM policy.
  8. How do I share my customer master keys (CMKs) across multiple AWS accounts?
    To grant another account access to your CMKs, create an IAM policy on the secondary account that grants access to use your CMKs.
  9. How do I set up AWS Trusted Advisor notifications?
    Learn how to receive free weekly email notifications from Trusted Advisor.
  10. How do I use AWS Key Management Service (AWS KMS) encryption context to protect the integrity of encrypted data?
    Encryption context name-value pairs used with AWS KMS encryption and decryption operations provide a method for checking ciphertext authenticity. Learn how to use encryption context to help protect your encrypted data.

The AWS Security Blog will publish an updated version of this list regularly going forward. You also can subscribe to the AWS Knowledge Center Videos playlist on YouTube.

– Maggie

New – Interactive AWS Cost Explorer API

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-interactive-aws-cost-explorer-api/

We launched the AWS Cost Explorer a couple of years ago in order to allow you to track, allocate, and manage your AWS costs. The response to that launch, and to additions that we have made since then, has been very positive. However our customers are, as Jeff Bezos has said, “beautifully, wonderfully, dissatisfied.”

I see this first-hand every day. We launch something and that launch inspires our customers to ask for even more. For example, with many customers going all-in and moving large parts of their IT infrastructure to the AWS Cloud, we’ve had many requests for the raw data that feeds into the Cost Explorer. These customers want to programmatically explore their AWS costs, update ledgers and accounting systems with per-application and per-department costs, and to build high-level dashboards that summarize spending. Some of these customers have been going to the trouble of extracting the data from the charts and reports provided by Cost Explorer!

New Cost Explorer API
Today we are making the underlying data that feeds into Cost Explorer available programmatically. The new Cost Explorer API gives you a set of functions that allow you do everything that I described above. You can retrieve cost and usage data that is filtered and grouped across multiple dimensions (Service, Linked Account, tag, Availability Zone, and so forth), aggregated by day or by month. This gives you the power to start simple (total monthly costs) and to refine your requests to any desired level of detail (writes to DynamoDB tables that have been tagged as production) while getting responses in seconds.

Here are the operations:

GetCostAndUsage – Retrieve cost and usage metrics for a single account or all accounts (master accounts in an organization have access to all member accounts) with filtering and grouping.

GetDimensionValues – Retrieve available filter values for a specified filter over a specified period of time.

GetTags – Retrieve available tag keys and tag values over a specified period of time.

GetReservationUtilization – Retrieve EC2 Reserved Instance utilization over a specified period of time, with daily or monthly granularity plus filtering and grouping.

I believe that these functions, and the data that they return, will give you the ability to do some really interesting things that will give you better insights into your business. For example, you could tag the resources used to support individual marketing campaigns or development projects and then deep-dive into the costs to measure business value. You how have the potential to know, down to the penny, how much you spend on infrastructure for important events like Cyber Monday or Black Friday.

Things to Know
Here are a couple of things to keep in mind as you start to think about ways to make use of the API:

Grouping – The Cost Explorer web application provides you with one level of grouping; the APIs give you two. For example you could group costs or RI utilization by Service and then by Region.

Pagination – The functions can return very large amounts of data and follow the AWS-wide model for pagination by including a nextPageToken if additional data is available. You simply call the same function again, supplying the token, to move forward.

Regions – The service endpoint is in the US East (Northern Virginia) Region and returns usage data for all public AWS Regions.

Pricing – Each API call costs $0.01. To put this into perspective, let’s say you use this API to build a dashboard and it gets 1000 hits per month from your users. Your operating cost for the dashboard should be $10 or so; this is far less expensive than setting up your own systems to extract & ingest the data and respond to interactive queries.

The Cost Explorer API is available now and you can start using it today. To learn more, read about the Cost Explorer API.


Ethereum Parity Bug Destroys Over $250 Million In Tokens

Post Syndicated from Darknet original https://www.darknet.org.uk/2017/11/ethereum-parity-bug-destroys-250-million-tokens/?utm_source=rss&utm_medium=social&utm_campaign=darknetfeed

Ethereum Parity Bug Destroys Over $250 Million In Tokens

If you are into cryptocurrency or blockchain at all, you will have heard about the Ethereum Parity Bug that has basically thrown $280 Million value or more of Ethereum tokens in the bin.

It’s a bit of a mess really, and a mistake by the developers who introduced it after fixing another bug back in July to do with multisig wallets (wallets which multiple people have to agree to transactions).

You can see the thread on Github here: anyone can kill your contract #6995

There’s a lot of hair-pulling among Ethereum alt-coin hoarders today – after a programming blunder in Parity’s wallet software let one person bin $280m of the digital currency belonging to scores of strangers, probably permanently.

Read the rest of Ethereum Parity Bug Destroys Over $250 Million In Tokens now! Only available at Darknet.

Just in Case You Missed It: Catching Up on Some Recent AWS Launches

Post Syndicated from Tara Walker original https://aws.amazon.com/blogs/aws/just-in-case-you-missed-it-catching-up-on-some-recent-aws-launches/

So many launches and cloud innovations, that you simply may not believe.  In order to catch up on some service launches and features, this post will be a round-up of some cool releases that happened this summer and through the end of September.

The launches and features I want to share with you today are:

  • AWS IAM for Authenticating Database Users for RDS MySQL and Amazon Aurora
  • Amazon SES Reputation Dashboard
  • Amazon SES Open and Click Tracking Metrics
  • Serverless Image Handler by the Solutions Builder Team
  • AWS Ops Automator by the Solutions Builder Team

Let’s dive in, shall we!

AWS IAM for Authenticating Database Users for RDS MySQL and Amazon Aurora

Wished you could manage access to your Amazon RDS database instances and clusters using AWS IAM? Well, wish no longer. Amazon RDS has launched the ability for you to use IAM to manage database access for Amazon RDS for MySQL and Amazon Aurora DB.

What I like most about this new service feature is, it’s very easy to get started.  To enable database user authentication using IAM, you would select a checkbox Enable IAM DB Authentication when creating, modifying, or restoring your DB instance or cluster. You can enable IAM access using the RDS console, the AWS CLI, and/or the Amazon RDS API.

After configuring the database for IAM authentication, client applications authenticate to the database engine by providing temporary security credentials generated by the IAM Security Token Service. These credentials can be used instead of providing a password to the database engine.

You can learn more about using IAM to provide targeted permissions and authentication to MySQL and Aurora by reviewing the Amazon RDS user guide.

Amazon SES Reputation Dashboard

In order to aid Amazon Simple Email Service customers’ in utilizing best practice guidelines for sending email, I am thrilled to announce we launched the Reputation Dashboard to provide comprehensive reporting on email sending health. To aid in proactively managing emails being sent, customers now have visibility into overall account health, sending metrics, and compliance or enforcement status.

The Reputation Dashboard will provide the following information:

  • Account status: A description of your account health status.
    • Healthy – No issues currently impacting your account.
    • Probation – Account is on probation; Issues causing probation must be resolved to prevent suspension
    • Pending end of probation decision – Your account is on probation. Amazon SES team member must review your account prior to action.
    • Shutdown – Your account has been shut down. No email will be able to be sent using Amazon SES.
    • Pending shutdown – Your account is on probation and issues causing probation are unresolved.
  • Bounce Rate: Percentage of emails sent that have bounced and bounce rate status messages.
  • Complaint Rate: Percentage of emails sent that recipients have reported as spam and complaint rate status messages.
  • Notifications: Messages about other account reputation issues.

Amazon SES Open and Click Tracking Metrics

Another exciting feature recently added to Amazon SES is support for Email Open and Click Tracking Metrics. With Email Open and Click Tracking Metrics feature, SES customers can now track when email they’ve sent has been opened and track when links within the email have been clicked.  Using this SES feature will allow you to better track email campaign engagement and effectiveness.

How does this work?

When using the email open tracking feature, SES will add a transparent, miniature image into the emails that you choose to track. When the email is opened, the mail application client will load the aforementioned tracking which triggers an open track event with Amazon SES. For the email click (link) tracking, links in email and/or email templates are replaced with a custom link.  When the custom link is clicked, a click event is recorded in SES and the custom link will redirect the email user to the link destination of the original email.

You can take advantage of the new open tracking and click tracking features by creating a new configuration set or altering an existing configuration set within SES. After choosing either; Amazon SNS, Amazon CloudWatch, or Amazon Kinesis Firehose as the AWS service to receive the open and click metrics, you would only need to select a new configuration set to successfully enable these new features for any emails you want to send.

AWS Solutions: Serverless Image Handler & AWS Ops Automator

The AWS Solution Builder team has been hard at work helping to make it easier for you all to find answers to common architectural questions to aid in building and running applications on AWS. You can find these solutions on the AWS Answers page. Two new solutions released earlier this fall on AWS Answers are  Serverless Image Handler and the AWS Ops Automator.
Serverless Image Handler was developed to provide a solution to help customers dynamically process, manipulate, and optimize the handling of images on the AWS Cloud. The solution combines Amazon CloudFront for caching, AWS Lambda to dynamically retrieve images and make image modifications, and Amazon S3 bucket to store images. Additionally, the Serverless Image Handler leverages the open source image-processing suite, Thumbor, for additional image manipulation, processing, and optimization.

AWS Ops Automator solution helps you to automate manual tasks using time-based or event-based triggers to automatically such as snapshot scheduling by providing a framework for automated tasks and includes task audit trails, logging, resource selection, scaling, concurrency handling, task completion handing, and API request retries. The solution includes the following AWS services:

  • AWS CloudFormation: a templates to launches the core framework of microservices and solution generated task configurations
  • Amazon DynamoDB: a table which stores task configuration data to defines the event triggers, resources, and saves the results of the action and the errors.
  • Amazon CloudWatch Logs: provides logging to track warning and error messages
  • Amazon SNS: topic to send messages to a subscribed email address to which to send the logging information from the solution

Have fun exploring and coding.


Amazon ElastiCache for Redis Is Now a HIPAA Eligible Service and You Can Use It to Power Real-Time Healthcare Applications

Post Syndicated from Manan Goel original https://aws.amazon.com/blogs/security/now-you-can-use-amazon-elasticache-for-redis-a-hipaa-eligible-service-to-power-real-time-healthcare-applications/

HIPAA image

Amazon ElastiCache for Redis is now a HIPAA Eligible Service and has been added to the AWS Business Associate Addendum (BAA). This means you can use ElastiCache for Redis to help you power healthcare applications as well as process, maintain, and store protected health information (PHI). ElastiCache for Redis is a Redis-compatible, fully-managed, in-memory data store and cache in the cloud that provides sub-millisecond latency to power applications. Now you can use the speed, simplicity, and flexibility of ElastiCache for Redis to build secure, fast, and internet-scale healthcare applications.

ElastiCache for Redis with HIPAA eligibility is available for all current-generation instance node types and requires Redis engine version 3.2.6. You must ensure that nodes are configured to encrypt the data in transit and at rest, and to authenticate Redis commands before the engine executes them. See Architecting for HIPAA Security and Compliance on Amazon Web Services for information about how to configure Amazon HIPAA Eligible Services to store, process, and transmit PHI.

ElastiCache for Redis uses Advanced Encryption Standard (AES)-512 symmetric keys to encrypt data on disk. The Redis backups stored in Amazon S3 are encrypted with server-side encryption (SSE) using AES-256 symmetric keys. ElastiCache for Redis uses Transport Layer Security (TLS) to encrypt data in transit. It uses the Redis AUTH token that you provide at the time of Redis cluster creation to authenticate the Redis commands coming from clients. The AUTH token is encrypted using AWS Key Management Service.

There is no additional charge for using ElastiCache for Redis clusters with HIPAA eligibility. To get started, see HIPAA Compliance for Amazon ElastiCache for Redis.

– Manan

Grafana 4.6 Released

Post Syndicated from Blogs on Grafana Labs Blog original https://grafana.com/blog/2017/10/26/grafana-4.6-released/

Release Highlights

The Grafana 4.6 release contains some exciting and much anticipated new additions:

This is a big release so check out the other features and fixes in the Changelog section below.


Annotations provide a way to mark points on the graph with rich events. You can now add annotation events and regions right from the graph panel! Just hold CTRL/CMD + click or drag region to open the Add Annotation view. The
Annotations documentation is updated to include details on this new exciting feature.


Cloudwatch now supports alerting. You can now setup alert rules for any Cloudwatch metric!


Grafana v4.6 now ships with a built-in datasource plugin for PostgreSQL. Have logs or metric data in Postgres? You can now visualize that data and
define alert rules on it like any of our other data sources.


New enhancements include support for instant queries (for a single point in time instead of a time range) and improvements to query editor in the form of autocomplete for label names and label values.

This makes exploring and filtering Prometheus data much easier.


Here are just a few highlights from the Changelog.

New Features

  • Annotations: Add support for creating annotations from graph panel #8197
  • GCS: Adds support for Google Cloud Storage #8370 thx @chuhlomin
  • Prometheus: Adds /metrics endpoint for exposing Grafana metrics. #9187
  • Jaeger: Add support for open tracing using jaeger in Grafana. #9213
  • Unit types: New date & time unit types added, useful in singlestat to show dates & times. #3678, #6710, #2764
  • Prometheus: Add support for instant queries #5765, thx @mtanda
  • Cloudwatch: Add support for alerting using the cloudwatch datasource #8050, thx @mtanda
  • Pagerduty: Include triggering series in pagerduty notification #8479, thx @rickymoorhouse
  • Prometheus: Align $__interval with the step parameters. #9226, thx @alin-amana
  • Prometheus: Autocomplete for label name and label value #9208, thx @mtanda
  • Postgres: New Postgres data source #9209, thx @svenklemm
  • Datasources: Make datasource HTTP requests verify TLS by default. closes #9371, #5334, #8812, thx @mattbostock


  • SMTP: Make it possible to set specific HELO for smtp client. #9319
  • Alerting: Add diff and percent diff as series reducers #9386, thx @shanhuhai5739
  • Slack: Allow images to be uploaded to slack when Token is present #7175, thx @xginn8
  • Table: Add support for displaying the timestamp with milliseconds #9429, thx @s1061123
  • Hipchat: Add metrics, message and image to hipchat notifications #9110, thx @eloo
  • Kafka: Add support for sending alert notifications to kafka #7104, thx @utkarshcmu


  • Webpack: Changed from systemjs to webpack (see readme or building from source guide for new build instructions). Systemjs is still used to load plugins but now plugins can only import a limited set of dependencies. See PLUGIN_DEV.md for more details on how this can effect some plugins.


Head to the v4.6 download page for download links & instructions.


A big thanks to all the Grafana users who contribute by submitting PRs, bug reports, helping out on our community site and providing feedback!