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Analyzing AWS Cost and Usage Reports with Looker and Amazon Athena

Post Syndicated from Dillon Morrison original https://aws.amazon.com/blogs/big-data/analyzing-aws-cost-and-usage-reports-with-looker-and-amazon-athena/

This is a guest post by Dillon Morrison at Looker. Looker is, in their own words, “a new kind of analytics platform–letting everyone in your business make better decisions by getting reliable answers from a tool they can use.” 

As the breadth of AWS products and services continues to grow, customers are able to more easily move their technology stack and core infrastructure to AWS. One of the attractive benefits of AWS is the cost savings. Rather than paying upfront capital expenses for large on-premises systems, customers can instead pay variables expenses for on-demand services. To further reduce expenses AWS users can reserve resources for specific periods of time, and automatically scale resources as needed.

The AWS Cost Explorer is great for aggregated reporting. However, conducting analysis on the raw data using the flexibility and power of SQL allows for much richer detail and insight, and can be the better choice for the long term. Thankfully, with the introduction of Amazon Athena, monitoring and managing these costs is now easier than ever.

In the post, I walk through setting up the data pipeline for cost and usage reports, Amazon S3, and Athena, and discuss some of the most common levers for cost savings. I surface tables through Looker, which comes with a host of pre-built data models and dashboards to make analysis of your cost and usage data simple and intuitive.

Analysis with Athena

With Athena, there’s no need to create hundreds of Excel reports, move data around, or deploy clusters to house and process data. Athena uses Apache Hive’s DDL to create tables, and the Presto querying engine to process queries. Analysis can be performed directly on raw data in S3. Conveniently, AWS exports raw cost and usage data directly into a user-specified S3 bucket, making it simple to start querying with Athena quickly. This makes continuous monitoring of costs virtually seamless, since there is no infrastructure to manage. Instead, users can leverage the power of the Athena SQL engine to easily perform ad-hoc analysis and data discovery without needing to set up a data warehouse.

After the data pipeline is established, cost and usage data (the recommended billing data, per AWS documentation) provides a plethora of comprehensive information around usage of AWS services and the associated costs. Whether you need the report segmented by product type, user identity, or region, this report can be cut-and-sliced any number of ways to properly allocate costs for any of your business needs. You can then drill into any specific line item to see even further detail, such as the selected operating system, tenancy, purchase option (on-demand, spot, or reserved), and so on.

Walkthrough

By default, the Cost and Usage report exports CSV files, which you can compress using gzip (recommended for performance). There are some additional configuration options for tuning performance further, which are discussed below.

Prerequisites

If you want to follow along, you need the following resources:

Enable the cost and usage reports

First, enable the Cost and Usage report. For Time unit, select Hourly. For Include, select Resource IDs. All options are prompted in the report-creation window.

The Cost and Usage report dumps CSV files into the specified S3 bucket. Please note that it can take up to 24 hours for the first file to be delivered after enabling the report.

Configure the S3 bucket and files for Athena querying

In addition to the CSV file, AWS also creates a JSON manifest file for each cost and usage report. Athena requires that all of the files in the S3 bucket are in the same format, so we need to get rid of all these manifest files. If you’re looking to get started with Athena quickly, you can simply go into your S3 bucket and delete the manifest file manually, skip the automation described below, and move on to the next section.

To automate the process of removing the manifest file each time a new report is dumped into S3, which I recommend as you scale, there are a few additional steps. The folks at Concurrency labs wrote a great overview and set of scripts for this, which you can find in their GitHub repo.

These scripts take the data from an input bucket, remove anything unnecessary, and dump it into a new output bucket. We can utilize AWS Lambda to trigger this process whenever new data is dropped into S3, or on a nightly basis, or whatever makes most sense for your use-case, depending on how often you’re querying the data. Please note that enabling the “hourly” report means that data is reported at the hour-level of granularity, not that a new file is generated every hour.

Following these scripts, you’ll notice that we’re adding a date partition field, which isn’t necessary but improves query performance. In addition, converting data from CSV to a columnar format like ORC or Parquet also improves performance. We can automate this process using Lambda whenever new data is dropped in our S3 bucket. Amazon Web Services discusses columnar conversion at length, and provides walkthrough examples, in their documentation.

As a long-term solution, best practice is to use compression, partitioning, and conversion. However, for purposes of this walkthrough, we’re not going to worry about them so we can get up-and-running quicker.

Set up the Athena query engine

In your AWS console, navigate to the Athena service, and click “Get Started”. Follow the tutorial and set up a new database (we’ve called ours “AWS Optimizer” in this example). Don’t worry about configuring your initial table, per the tutorial instructions. We’ll be creating a new table for cost and usage analysis. Once you walked through the tutorial steps, you’ll be able to access the Athena interface, and can begin running Hive DDL statements to create new tables.

One thing that’s important to note, is that the Cost and Usage CSVs also contain the column headers in their first row, meaning that the column headers would be included in the dataset and any queries. For testing and quick set-up, you can remove this line manually from your first few CSV files. Long-term, you’ll want to use a script to programmatically remove this row each time a new file is dropped in S3 (every few hours typically). We’ve drafted up a sample script for ease of reference, which we run on Lambda. We utilize Lambda’s native ability to invoke the script whenever a new object is dropped in S3.

For cost and usage, we recommend using the DDL statement below. Since our data is in CSV format, we don’t need to use a SerDe, we can simply specify the “separatorChar, quoteChar, and escapeChar”, and the structure of the files (“TEXTFILE”). Note that AWS does have an OpenCSV SerDe as well, if you prefer to use that.

 

CREATE EXTERNAL TABLE IF NOT EXISTS cost_and_usage	 (
identity_LineItemId String,
identity_TimeInterval String,
bill_InvoiceId String,
bill_BillingEntity String,
bill_BillType String,
bill_PayerAccountId String,
bill_BillingPeriodStartDate String,
bill_BillingPeriodEndDate String,
lineItem_UsageAccountId String,
lineItem_LineItemType String,
lineItem_UsageStartDate String,
lineItem_UsageEndDate String,
lineItem_ProductCode String,
lineItem_UsageType String,
lineItem_Operation String,
lineItem_AvailabilityZone String,
lineItem_ResourceId String,
lineItem_UsageAmount String,
lineItem_NormalizationFactor String,
lineItem_NormalizedUsageAmount String,
lineItem_CurrencyCode String,
lineItem_UnblendedRate String,
lineItem_UnblendedCost String,
lineItem_BlendedRate String,
lineItem_BlendedCost String,
lineItem_LineItemDescription String,
lineItem_TaxType String,
product_ProductName String,
product_accountAssistance String,
product_architecturalReview String,
product_architectureSupport String,
product_availability String,
product_bestPractices String,
product_cacheEngine String,
product_caseSeverityresponseTimes String,
product_clockSpeed String,
product_currentGeneration String,
product_customerServiceAndCommunities String,
product_databaseEdition String,
product_databaseEngine String,
product_dedicatedEbsThroughput String,
product_deploymentOption String,
product_description String,
product_durability String,
product_ebsOptimized String,
product_ecu String,
product_endpointType String,
product_engineCode String,
product_enhancedNetworkingSupported String,
product_executionFrequency String,
product_executionLocation String,
product_feeCode String,
product_feeDescription String,
product_freeQueryTypes String,
product_freeTrial String,
product_frequencyMode String,
product_fromLocation String,
product_fromLocationType String,
product_group String,
product_groupDescription String,
product_includedServices String,
product_instanceFamily String,
product_instanceType String,
product_io String,
product_launchSupport String,
product_licenseModel String,
product_location String,
product_locationType String,
product_maxIopsBurstPerformance String,
product_maxIopsvolume String,
product_maxThroughputvolume String,
product_maxVolumeSize String,
product_maximumStorageVolume String,
product_memory String,
product_messageDeliveryFrequency String,
product_messageDeliveryOrder String,
product_minVolumeSize String,
product_minimumStorageVolume String,
product_networkPerformance String,
product_operatingSystem String,
product_operation String,
product_operationsSupport String,
product_physicalProcessor String,
product_preInstalledSw String,
product_proactiveGuidance String,
product_processorArchitecture String,
product_processorFeatures String,
product_productFamily String,
product_programmaticCaseManagement String,
product_provisioned String,
product_queueType String,
product_requestDescription String,
product_requestType String,
product_routingTarget String,
product_routingType String,
product_servicecode String,
product_sku String,
product_softwareType String,
product_storage String,
product_storageClass String,
product_storageMedia String,
product_technicalSupport String,
product_tenancy String,
product_thirdpartySoftwareSupport String,
product_toLocation String,
product_toLocationType String,
product_training String,
product_transferType String,
product_usageFamily String,
product_usagetype String,
product_vcpu String,
product_version String,
product_volumeType String,
product_whoCanOpenCases String,
pricing_LeaseContractLength String,
pricing_OfferingClass String,
pricing_PurchaseOption String,
pricing_publicOnDemandCost String,
pricing_publicOnDemandRate String,
pricing_term String,
pricing_unit String,
reservation_AvailabilityZone String,
reservation_NormalizedUnitsPerReservation String,
reservation_NumberOfReservations String,
reservation_ReservationARN String,
reservation_TotalReservedNormalizedUnits String,
reservation_TotalReservedUnits String,
reservation_UnitsPerReservation String,
resourceTags_userName String,
resourceTags_usercostcategory String  


)
    ROW FORMAT DELIMITED
      FIELDS TERMINATED BY ','
      ESCAPED BY '\\'
      LINES TERMINATED BY '\n'

STORED AS TEXTFILE
    LOCATION 's3://<<your bucket name>>';

Once you’ve successfully executed the command, you should see a new table named “cost_and_usage” with the below properties. Now we’re ready to start executing queries and running analysis!

Start with Looker and connect to Athena

Setting up Looker is a quick process, and you can try it out for free here (or download from Amazon Marketplace). It takes just a few seconds to connect Looker to your Athena database, and Looker comes with a host of pre-built data models and dashboards to make analysis of your cost and usage data simple and intuitive. After you’re connected, you can use the Looker UI to run whatever analysis you’d like. Looker translates this UI to optimized SQL, so any user can execute and visualize queries for true self-service analytics.

Major cost saving levers

Now that the data pipeline is configured, you can dive into the most popular use cases for cost savings. In this post, I focus on:

  • Purchasing Reserved Instances vs. On-Demand Instances
  • Data transfer costs
  • Allocating costs over users or other Attributes (denoted with resource tags)

On-Demand, Spot, and Reserved Instances

Purchasing Reserved Instances vs On-Demand Instances is arguably going to be the biggest cost lever for heavy AWS users (Reserved Instances run up to 75% cheaper!). AWS offers three options for purchasing instances:

  • On-Demand—Pay as you use.
  • Spot (variable cost)—Bid on spare Amazon EC2 computing capacity.
  • Reserved Instances—Pay for an instance for a specific, allotted period of time.

When purchasing a Reserved Instance, you can also choose to pay all-upfront, partial-upfront, or monthly. The more you pay upfront, the greater the discount.

If your company has been using AWS for some time now, you should have a good sense of your overall instance usage on a per-month or per-day basis. Rather than paying for these instances On-Demand, you should try to forecast the number of instances you’ll need, and reserve them with upfront payments.

The total amount of usage with Reserved Instances versus overall usage with all instances is called your coverage ratio. It’s important not to confuse your coverage ratio with your Reserved Instance utilization. Utilization represents the amount of reserved hours that were actually used. Don’t worry about exceeding capacity, you can still set up Auto Scaling preferences so that more instances get added whenever your coverage or utilization crosses a certain threshold (we often see a target of 80% for both coverage and utilization among savvy customers).

Calculating the reserved costs and coverage can be a bit tricky with the level of granularity provided by the cost and usage report. The following query shows your total cost over the last 6 months, broken out by Reserved Instance vs other instance usage. You can substitute the cost field for usage if you’d prefer. Please note that you should only have data for the time period after the cost and usage report has been enabled (though you can opt for up to 3 months of historical data by contacting your AWS Account Executive). If you’re just getting started, this query will only show a few days.

 

SELECT 
	DATE_FORMAT(from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate),'%Y-%m') AS "cost_and_usage.usage_start_month",
	COALESCE(SUM(cost_and_usage.lineitem_unblendedcost ), 0) AS "cost_and_usage.total_unblended_cost",
	COALESCE(SUM(CASE WHEN (CASE
         WHEN cost_and_usage.lineitem_lineitemtype = 'DiscountedUsage' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'RIFee' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'Fee' THEN 'RI Line Item'
         ELSE 'Non RI Line Item'
        END = 'RI Line Item') THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0) AS "cost_and_usage.total_reserved_unblended_cost",
	1.0 * (COALESCE(SUM(CASE WHEN (CASE
         WHEN cost_and_usage.lineitem_lineitemtype = 'DiscountedUsage' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'RIFee' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'Fee' THEN 'RI Line Item'
         ELSE 'Non RI Line Item'
        END = 'RI Line Item') THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0)) / NULLIF((COALESCE(SUM(cost_and_usage.lineitem_unblendedcost ), 0)),0)  AS "cost_and_usage.percent_spend_on_ris",
	COALESCE(SUM(CASE WHEN (CASE
         WHEN cost_and_usage.lineitem_lineitemtype = 'DiscountedUsage' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'RIFee' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'Fee' THEN 'RI Line Item'
         ELSE 'Non RI Line Item'
        END = 'Non RI Line Item') THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0) AS "cost_and_usage.total_non_reserved_unblended_cost",
	1.0 * (COALESCE(SUM(CASE WHEN (CASE
         WHEN cost_and_usage.lineitem_lineitemtype = 'DiscountedUsage' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'RIFee' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'Fee' THEN 'RI Line Item'
         ELSE 'Non RI Line Item'
        END = 'Non RI Line Item') THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0)) / NULLIF((COALESCE(SUM(cost_and_usage.lineitem_unblendedcost ), 0)),0)  AS "cost_and_usage.percent_spend_on_non_ris"
FROM aws_optimizer.cost_and_usage  AS cost_and_usage

WHERE 
	(((from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate)) >= ((DATE_ADD('month', -5, DATE_TRUNC('MONTH', CAST(NOW() AS DATE))))) AND (from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate)) < ((DATE_ADD('month', 6, DATE_ADD('month', -5, DATE_TRUNC('MONTH', CAST(NOW() AS DATE))))))))
GROUP BY 1
ORDER BY 2 DESC
LIMIT 500

The resulting table should look something like the image below (I’m surfacing tables through Looker, though the same table would result from querying via command line or any other interface).

With a BI tool, you can create dashboards for easy reference and monitoring. New data is dumped into S3 every few hours, so your dashboards can update several times per day.

It’s an iterative process to understand the appropriate number of Reserved Instances needed to meet your business needs. After you’ve properly integrated Reserved Instances into your purchasing patterns, the savings can be significant. If your coverage is consistently below 70%, you should seriously consider adjusting your purchase types and opting for more Reserved instances.

Data transfer costs

One of the great things about AWS data storage is that it’s incredibly cheap. Most charges often come from moving and processing that data. There are several different prices for transferring data, broken out largely by transfers between regions and availability zones. Transfers between regions are the most costly, followed by transfers between Availability Zones. Transfers within the same region and same availability zone are free unless using elastic or public IP addresses, in which case there is a cost. You can find more detailed information in the AWS Pricing Docs. With this in mind, there are several simple strategies for helping reduce costs.

First, since costs increase when transferring data between regions, it’s wise to ensure that as many services as possible reside within the same region. The more you can localize services to one specific region, the lower your costs will be.

Second, you should maximize the data you’re routing directly within AWS services and IP addresses. Transfers out to the open internet are the most costly and least performant mechanisms of data transfers, so it’s best to keep transfers within AWS services.

Lastly, data transfers between private IP addresses are cheaper than between elastic or public IP addresses, so utilizing private IP addresses as much as possible is the most cost-effective strategy.

The following query provides a table depicting the total costs for each AWS product, broken out transfer cost type. Substitute the “lineitem_productcode” field in the query to segment the costs by any other attribute. If you notice any unusually high spikes in cost, you’ll need to dig deeper to understand what’s driving that spike: location, volume, and so on. Drill down into specific costs by including “product_usagetype” and “product_transfertype” in your query to identify the types of transfer costs that are driving up your bill.

SELECT 
	cost_and_usage.lineitem_productcode  AS "cost_and_usage.product_code",
	COALESCE(SUM(cost_and_usage.lineitem_unblendedcost), 0) AS "cost_and_usage.total_unblended_cost",
	COALESCE(SUM(CASE WHEN REGEXP_LIKE(cost_and_usage.product_usagetype, 'DataTransfer')    THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0) AS "cost_and_usage.total_data_transfer_cost",
	COALESCE(SUM(CASE WHEN REGEXP_LIKE(cost_and_usage.product_usagetype, 'DataTransfer-In')    THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0) AS "cost_and_usage.total_inbound_data_transfer_cost",
	COALESCE(SUM(CASE WHEN REGEXP_LIKE(cost_and_usage.product_usagetype, 'DataTransfer-Out')    THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0) AS "cost_and_usage.total_outbound_data_transfer_cost"
FROM aws_optimizer.cost_and_usage  AS cost_and_usage

WHERE 
	(((from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate)) >= ((DATE_ADD('month', -5, DATE_TRUNC('MONTH', CAST(NOW() AS DATE))))) AND (from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate)) < ((DATE_ADD('month', 6, DATE_ADD('month', -5, DATE_TRUNC('MONTH', CAST(NOW() AS DATE))))))))
GROUP BY 1
ORDER BY 2 DESC
LIMIT 500

When moving between regions or over the open web, many data transfer costs also include the origin and destination location of the data movement. Using a BI tool with mapping capabilities, you can get a nice visual of data flows. The point at the center of the map is used to represent external data flows over the open internet.

Analysis by tags

AWS provides the option to apply custom tags to individual resources, so you can allocate costs over whatever customized segment makes the most sense for your business. For a SaaS company that hosts software for customers on AWS, maybe you’d want to tag the size of each customer. The following query uses custom tags to display the reserved, data transfer, and total cost for each AWS service, broken out by tag categories, over the last 6 months. You’ll want to substitute the cost_and_usage.resourcetags_customersegment and cost_and_usage.customer_segment with the name of your customer field.

 

SELECT * FROM (
SELECT *, DENSE_RANK() OVER (ORDER BY z___min_rank) as z___pivot_row_rank, RANK() OVER (PARTITION BY z__pivot_col_rank ORDER BY z___min_rank) as z__pivot_col_ordering FROM (
SELECT *, MIN(z___rank) OVER (PARTITION BY "cost_and_usage.product_code") as z___min_rank FROM (
SELECT *, RANK() OVER (ORDER BY CASE WHEN z__pivot_col_rank=1 THEN (CASE WHEN "cost_and_usage.total_unblended_cost" IS NOT NULL THEN 0 ELSE 1 END) ELSE 2 END, CASE WHEN z__pivot_col_rank=1 THEN "cost_and_usage.total_unblended_cost" ELSE NULL END DESC, "cost_and_usage.total_unblended_cost" DESC, z__pivot_col_rank, "cost_and_usage.product_code") AS z___rank FROM (
SELECT *, DENSE_RANK() OVER (ORDER BY CASE WHEN "cost_and_usage.customer_segment" IS NULL THEN 1 ELSE 0 END, "cost_and_usage.customer_segment") AS z__pivot_col_rank FROM (
SELECT 
	cost_and_usage.lineitem_productcode  AS "cost_and_usage.product_code",
	cost_and_usage.resourcetags_customersegment  AS "cost_and_usage.customer_segment",
	COALESCE(SUM(cost_and_usage.lineitem_unblendedcost ), 0) AS "cost_and_usage.total_unblended_cost",
	1.0 * (COALESCE(SUM(CASE WHEN REGEXP_LIKE(cost_and_usage.product_usagetype, 'DataTransfer')    THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0)) / NULLIF((COALESCE(SUM(cost_and_usage.lineitem_unblendedcost ), 0)),0)  AS "cost_and_usage.percent_spend_data_transfers_unblended",
	1.0 * (COALESCE(SUM(CASE WHEN (CASE
         WHEN cost_and_usage.lineitem_lineitemtype = 'DiscountedUsage' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'RIFee' THEN 'RI Line Item'
         WHEN cost_and_usage.lineitem_lineitemtype = 'Fee' THEN 'RI Line Item'
         ELSE 'Non RI Line Item'
        END = 'Non RI Line Item') THEN cost_and_usage.lineitem_unblendedcost  ELSE NULL END), 0)) / NULLIF((COALESCE(SUM(cost_and_usage.lineitem_unblendedcost ), 0)),0)  AS "cost_and_usage.unblended_percent_spend_on_ris"
FROM aws_optimizer.cost_and_usage_raw  AS cost_and_usage

WHERE 
	(((from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate)) >= ((DATE_ADD('month', -5, DATE_TRUNC('MONTH', CAST(NOW() AS DATE))))) AND (from_iso8601_timestamp(cost_and_usage.lineitem_usagestartdate)) < ((DATE_ADD('month', 6, DATE_ADD('month', -5, DATE_TRUNC('MONTH', CAST(NOW() AS DATE))))))))
GROUP BY 1,2) ww
) bb WHERE z__pivot_col_rank <= 16384
) aa
) xx
) zz
 WHERE z___pivot_row_rank <= 500 OR z__pivot_col_ordering = 1 ORDER BY z___pivot_row_rank

The resulting table in this example looks like the results below. In this example, you can tell that we’re making poor use of Reserved Instances because they represent such a small portion of our overall costs.

Again, using a BI tool to visualize these costs and trends over time makes the analysis much easier to consume and take action on.

Summary

Saving costs on your AWS spend is always an iterative, ongoing process. Hopefully with these queries alone, you can start to understand your spending patterns and identify opportunities for savings. However, this is just a peek into the many opportunities available through analysis of the Cost and Usage report. Each company is different, with unique needs and usage patterns. To achieve maximum cost savings, we encourage you to set up an analytics environment that enables your team to explore all potential cuts and slices of your usage data, whenever it’s necessary. Exploring different trends and spikes across regions, services, user types, etc. helps you gain comprehensive understanding of your major cost levers and consistently implement new cost reduction strategies.

Note that all of the queries and analysis provided in this post were generated using the Looker data platform. If you’re already a Looker customer, you can get all of this analysis, additional pre-configured dashboards, and much more using Looker Blocks for AWS.

About the Author

Dillon Morrison leads the Platform Ecosystem at Looker. He enjoys exploring new technologies and architecting the most efficient data solutions for the business needs of his company and their customers. In his spare time, you’ll find Dillon rock climbing in the Bay Area or nose deep in the docs of the latest AWS product release at his favorite cafe (“Arlequin in SF is unbeatable!”).

 

 

 

Raspbian Stretch has arrived for Raspberry Pi

Post Syndicated from Simon Long original https://www.raspberrypi.org/blog/raspbian-stretch/

It’s now just under two years since we released the Jessie version of Raspbian. Those of you who know that Debian run their releases on a two-year cycle will therefore have been wondering when we might be releasing the next version, codenamed Stretch. Well, wonder no longer – Raspbian Stretch is available for download today!

Disney Pixar Toy Story Raspbian Stretch Raspberry Pi

Debian releases are named after characters from Disney Pixar’s Toy Story trilogy. In case, like me, you were wondering: Stretch is a purple octopus from Toy Story 3. Hi, Stretch!

The differences between Jessie and Stretch are mostly under-the-hood optimisations, and you really shouldn’t notice any differences in day-to-day use of the desktop and applications. (If you’re really interested, the technical details are in the Debian release notes here.)

However, we’ve made a few small changes to our image that are worth mentioning.

New versions of applications

Version 3.0.1 of Sonic Pi is included – this includes a lot of new functionality in terms of input/output. See the Sonic Pi release notes for more details of exactly what has changed.

Raspbian Stretch Raspberry Pi

The Chromium web browser has been updated to version 60, the most recent stable release. This offers improved memory usage and more efficient code, so you may notice it running slightly faster than before. The visual appearance has also been changed very slightly.

Raspbian Stretch Raspberry Pi

Bluetooth audio

In Jessie, we used PulseAudio to provide support for audio over Bluetooth, but integrating this with the ALSA architecture used for other audio sources was clumsy. For Stretch, we are using the bluez-alsa package to make Bluetooth audio work with ALSA itself. PulseAudio is therefore no longer installed by default, and the volume plugin on the taskbar will no longer start and stop PulseAudio. From a user point of view, everything should still work exactly as before – the only change is that if you still wish to use PulseAudio for some other reason, you will need to install it yourself.

Better handling of other usernames

The default user account in Raspbian has always been called ‘pi’, and a lot of the desktop applications assume that this is the current user. This has been changed for Stretch, so now applications like Raspberry Pi Configuration no longer assume this to be the case. This means, for example, that the option to automatically log in as the ‘pi’ user will now automatically log in with the name of the current user instead.

One other change is how sudo is handled. By default, the ‘pi’ user is set up with passwordless sudo access. We are no longer assuming this to be the case, so now desktop applications which require sudo access will prompt for the password rather than simply failing to work if a user without passwordless sudo uses them.

Scratch 2 SenseHAT extension

In the last Jessie release, we added the offline version of Scratch 2. While Scratch 2 itself hasn’t changed for this release, we have added a new extension to allow the SenseHAT to be used with Scratch 2. Look under ‘More Blocks’ and choose ‘Add an Extension’ to load the extension.

This works with either a physical SenseHAT or with the SenseHAT emulator. If a SenseHAT is connected, the extension will control that in preference to the emulator.

Raspbian Stretch Raspberry Pi

Fix for Broadpwn exploit

A couple of months ago, a vulnerability was discovered in the firmware of the BCM43xx wireless chipset which is used on Pi 3 and Pi Zero W; this potentially allows an attacker to take over the chip and execute code on it. The Stretch release includes a patch that addresses this vulnerability.

There is also the usual set of minor bug fixes and UI improvements – I’ll leave you to spot those!

How to get Raspbian Stretch

As this is a major version upgrade, we recommend using a clean image; these are available from the Downloads page on our site as usual.

Upgrading an existing Jessie image is possible, but is not guaranteed to work in every circumstance. If you wish to try upgrading a Jessie image to Stretch, we strongly recommend taking a backup first – we can accept no responsibility for loss of data from a failed update.

To upgrade, first modify the files /etc/apt/sources.list and /etc/apt/sources.list.d/raspi.list. In both files, change every occurrence of the word ‘jessie’ to ‘stretch’. (Both files will require sudo to edit.)

Then open a terminal window and execute

sudo apt-get update
sudo apt-get -y dist-upgrade

Answer ‘yes’ to any prompts. There may also be a point at which the install pauses while a page of information is shown on the screen – hold the ‘space’ key to scroll through all of this and then hit ‘q’ to continue.

Finally, if you are not using PulseAudio for anything other than Bluetooth audio, remove it from the image by entering

sudo apt-get -y purge pulseaudio*

The post Raspbian Stretch has arrived for Raspberry Pi appeared first on Raspberry Pi.

What’s the Diff: Programs, Processes, and Threads

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/whats-the-diff-programs-processes-and-threads/

let's talk about Threads

How often have you heard the term threading in relation to a computer program, but you weren’t exactly sure what it meant? How about processes? You likely understand that a thread is somehow closely related to a program and a process, but if you’re not a computer science major, maybe that’s as far as your understanding goes.

Knowing what these terms mean is absolutely essential if you are a programmer, but an understanding of them also can be useful to the average computer user. Being able to look at and understand the Activity Monitor on the Macintosh, the Task Manager on Windows, or Top on Linux can help you troubleshoot which programs are causing problems on your computer, or whether you might need to install more memory to make your system run better.

Let’s take a few minutes to delve into the world of computer programs and sort out what these terms mean. We’ll simplify and generalize some of the ideas, but the general concepts we cover should help clarify the difference between the terms.

Programs

First of all, you probably are aware that a program is the code that is stored on your computer that is intended to fulfill a certain task. There are many types of programs, including programs that help your computer function and are part of the operating system, and other programs that fulfill a particular job. These task-specific programs are also known as “applications,” and can include programs such as word processing, web browsing, or emailing a message to another computer.

Program

Programs are typically stored on disk or in non-volatile memory in a form that can be executed by your computer. Prior to that, they are created using a programming language such as C, Lisp, Pascal, or many others using instructions that involve logic, data and device manipulation, recurrence, and user interaction. The end result is a text file of code that is compiled into binary form (1’s and 0’s) in order to run on the computer. Another type of program is called “interpreted,” and instead of being compiled in advance in order to run, is interpreted into executable code at the time it is run. Some common, typically interpreted programming languages, are Python, PHP, JavaScript, and Ruby.

The end result is the same, however, in that when a program is run, it is loaded into memory in binary form. The computer’s CPU (Central Processing Unit) understands only binary instructions, so that’s the form the program needs to be in when it runs.

Perhaps you’ve heard the programmer’s joke, “There are only 10 types of people in the world, those who understand binary, and those who don’t.”

Binary is the native language of computers because an electrical circuit at its basic level has two states, on or off, represented by a one or a zero. In the common numbering system we use every day, base 10, each digit position can be anything from 0 to 9. In base 2 (or binary), each position is either a 0 or a 1. (In a future blog post we might cover quantum computing, which goes beyond the concept of just 1’s and 0’s in computing.)

Decimal—Base 10 Binary—Base 2
0 0000
1 0001
2 0010
3 0011
4 0100
5 0101
6 0110
7 0111
8 1000
9 1001

How Processes Work

The program has been loaded into the computer’s memory in binary form. Now what?

An executing program needs more than just the binary code that tells the computer what to do. The program needs memory and various operating system resources that it needs in order to run. A “process” is what we call a program that has been loaded into memory along with all the resources it needs to operate. The “operating system” is the brains behind allocating all these resources, and comes in different flavors such as macOS, iOS, Microsoft Windows, Linux, and Android. The OS handles the task of managing the resources needed to turn your program into a running process.

Some essential resources every process needs are registers, a program counter, and a stack. The “registers” are data holding places that are part of the computer processor (CPU). A register may hold an instruction, a storage address, or other kind of data needed by the process. The “program counter,” also called the “instruction pointer,” keeps track of where a computer is in its program sequence. The “stack” is a data structure that stores information about the active subroutines of a computer program and is used as scratch space for the process. It is distinguished from dynamically allocated memory for the process that is known as “the heap.”

diagram of how processes work

There can be multiple instances of a single program, and each instance of that running program is a process. Each process has a separate memory address space, which means that a process runs independently and is isolated from other processes. It cannot directly access shared data in other processes. Switching from one process to another requires some time (relatively) for saving and loading registers, memory maps, and other resources.

This independence of processes is valuable because the operating system tries its best to isolate processes so that a problem with one process doesn’t corrupt or cause havoc with another process. You’ve undoubtedly run into the situation in which one application on your computer freezes or has a problem and you’ve been able to quit that program without affecting others.

How Threads Work

So, are you still with us? We finally made it to threads!

A thread is the unit of execution within a process. A process can have anywhere from just one thread to many threads.

Process vs. Thread

diagram of threads in a process over time

When a process starts, it is assigned memory and resources. Each thread in the process shares that memory and resources. In single-threaded processes, the process contains one thread. The process and the thread are one and the same, and there is only one thing happening.

In multithreaded processes, the process contains more than one thread, and the process is accomplishing a number of things at the same time (technically, it’s almost at the same time—read more on that in the “What about Parallelism and Concurrency?” section below).

diagram of single and multi-treaded process

We talked about the two types of memory available to a process or a thread, the stack and the heap. It is important to distinguish between these two types of process memory because each thread will have its own stack, but all the threads in a process will share the heap.

Threads are sometimes called lightweight processes because they have their own stack but can access shared data. Because threads share the same address space as the process and other threads within the process, the operational cost of communication between the threads is low, which is an advantage. The disadvantage is that a problem with one thread in a process will certainly affect other threads and the viability of the process itself.

Threads vs. Processes

So to review:

  1. The program starts out as a text file of programming code,
  2. The program is compiled or interpreted into binary form,
  3. The program is loaded into memory,
  4. The program becomes one or more running processes.
  5. Processes are typically independent of each other,
  6. While threads exist as the subset of a process.
  7. Threads can communicate with each other more easily than processes can,
  8. But threads are more vulnerable to problems caused by other threads in the same process.

Processes vs. Threads — Advantages and Disadvantages

Process Thread
Processes are heavyweight operations Threads are lighter weight operations
Each process has its own memory space Threads use the memory of the process they belong to
Inter-process communication is slow as processes have different memory addresses Inter-thread communication can be faster than inter-process communication because threads of the same process share memory with the process they belong to
Context switching between processes is more expensive Context switching between threads of the same process is less expensive
Processes don’t share memory with other processes Threads share memory with other threads of the same process

What about Concurrency and Parallelism?

A question you might ask is whether processes or threads can run at the same time. The answer is: it depends. On a system with multiple processors or CPU cores (as is common with modern processors), multiple processes or threads can be executed in parallel. On a single processor, though, it is not possible to have processes or threads truly executing at the same time. In this case, the CPU is shared among running processes or threads using a process scheduling algorithm that divides the CPU’s time and yields the illusion of parallel execution. The time given to each task is called a “time slice.” The switching back and forth between tasks happens so fast it is usually not perceptible. The terms parallelism (true operation at the same time) and concurrency (simulated operation at the same time), distinguish between the two type of real or approximate simultaneous operation.

diagram of concurrency and parallelism

Why Choose Process over Thread, or Thread over Process?

So, how would a programmer choose between a process and a thread when creating a program in which she wants to execute multiple tasks at the same time? We’ve covered some of the differences above, but let’s look at a real world example with a program that many of us use, Google Chrome.

When Google was designing the Chrome browser, they needed to decide how to handle the many different tasks that needed computer, communications, and network resources at the same time. Each browser window or tab communicates with multiple servers on the internet to retrieve text, programs, graphics, audio, video, and other resources, and renders that data for display and interaction with the user. In addition, the browser can open many windows, each with many tasks.

Google had to decide how to handle that separation of tasks. They chose to run each browser window in Chrome as a separate process rather than a thread or many threads, as is common with other browsers. Doing that brought Google a number of benefits. Running each window as a process protects the overall application from bugs and glitches in the rendering engine and restricts access from each rendering engine process to others and to the rest of the system. Isolating JavaScript programs in a process prevents them from running away with too much CPU time and memory, and making the entire browser non-responsive.

Google made the calculated trade-off with a multi-processing design as starting a new process for each browser window has a higher fixed cost in memory and resources than using threads. They were betting that their approach would end up with less memory bloat overall.

Using processes instead of threads provides better memory usage when memory gets low. An inactive window is treated as a lower priority by the operating system and becomes eligible to be swapped to disk when memory is needed for other processes, helping to keep the user-visible windows more responsive. If the windows were threaded, it would be more difficult to separate the used and unused memory as cleanly, wasting both memory and performance.

You can read more about Google’s design decisions on Google’s Chromium Blog or on the Chrome Introduction Comic.

The screen capture below shows the Google Chrome processes running on a MacBook Air with many tabs open. Some Chrome processes are using a fair amount of CPU time and resources, and some are using very little. You can see that each process also has many threads running as well.

activity monitor of Google Chrome

The Activity Monitor or Task Manager on your system can be a valuable ally in helping fine-tune your computer or troubleshooting problems. If your computer is running slowly, or a program or browser window isn’t responding for a while, you can check its status using the system monitor. Sometimes you’ll see a process marked as “Not Responding.” Try quitting that process and see if your system runs better. If an application is a memory hog, you might consider choosing a different application that will accomplish the same task.

Windows Task Manager view

Made it This Far?

We hope this Tron-like dive into the fascinating world of computer programs, processes, and threads has helped clear up some questions you might have had.

The next time your computer is running slowly or an application is acting up, you know your assignment. Fire up the system monitor and take a look under the hood to see what’s going on. You’re in charge now.

We love to hear from you

Are you still confused? Have questions? If so, please let us know in the comments. And feel free to suggest topics for future blog posts.

The post What’s the Diff: Programs, Processes, and Threads appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Roku Gets Tough on Pirate Channels, Warns Users

Post Syndicated from Ernesto original https://torrentfreak.com/roku-gets-tough-on-pirate-channels-warns-users-170815/

In recent years it has become much easier to stream movies and TV-shows over the Internet.

Legal services such as Netflix and HBO are flourishing, but there’s also a darker side to this streaming epidemic. Millions of people are streaming from unauthorized sources, often paired with perfectly legal streaming platforms and devices.

Hollywood insiders have dubbed this trend “Piracy 3.0” are actively working with stakeholders to address the threat. One of the companies rightsholders are working with is Roku, known for its easy-to-use media players.

Earlier this year Roku was harshly confronted with this new piracy crackdown when a Mexican court ordered local retailers to take its media player off the shelves. While this legal battle isn’t over yet, it was clear to Roku that misuse of its platform wasn’t without consequences.

While Roku never permitted any infringing content, it appears that the company has recently made some adjustments to better deal with the problem, or at least clarify its stance.

Pirate content generally doesn’t show up in the official Roku Channel Store but is directly loaded onto the device through third-party “private” channels. A few weeks ago, Roku renamed these “private” channels to “non-certified” channels, while making it very clear that copyright infringement is not allowed.

A “WARNING!” message that pops up during the installation of these third-party channels stresses that Roku has no control over the content. In addition, the company notes that these channels may be removed if it links to copyright infringing content.

Roku Warning

“By continuing, you acknowledge you are accessing a non-certified channel that may include content that is offensive or inappropriate for some audiences,” Roku’s warning reads.

“Moreover, if Roku determines that this channel violates copyright, contains illegal content, or otherwise violates Roku’s terms and conditions, then ROKU MAY REMOVE THIS CHANNEL WITHOUT PRIOR NOTICE.”

TorrentFreak reached out to Roku to find out how they plan to enforce this policy, but we have yet to hear back. According to Cord Cutters News, several piracy channels have already been removed recently, with other developers opting to leave the platform.

Roku’s General Counsel Steve Kay previously informed us that the company is taking the piracy problem seriously. Together with various stakeholders, they are working hard to address the problem.

“We actively work to prevent third-parties from using our platform to distribute copyright infringing content. Moreover, we have been actively working with other industry stakeholders on a wide range of anti-piracy initiatives,” Kay said.

Roku is not the only platform dealing with the piracy epidemic, the popular media player software Kodi is in the same boat. Kodi has also taken an active anti-piracy stance but they’re not banning any add-ons. They believe it would be pointless due to the open source nature of their software.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

OK Google, be aesthetically pleasing

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/aesthetically-pleasing-ok-google/

Maker Andrew Jones took a Raspberry Pi and the Google Assistant SDK and created a gorgeous-looking, and highly functional, alternative to store-bought smart speakers.

Raspberry Pi Google AI Assistant

In this video I get an “Ok Google” voice activated AI assistant running on a raspberry pi. I also hand make a nice wooden box for it to live in.

OK Google, what are you?

Google Assistant is software of the same ilk as Amazon’s Alexa, Apple’s Siri and Microsoft’s Cortana. It’s a virtual assistant that allows you to request information, play audio, and control smart home devices via voice commands.

Infinite Looping Siri, Alexa and Google Home

One can barely see the iPhone’s screen. That’s because I have a privacy protection screen. Sorry, did not check the camera angle. Learn how to create your own loop, why we put Cortana out of the loop, and how to train Siri to an artificial voice: https://www.danrl.com/2016/12/01/looping-ais-siri-alexa-google-home.html

You probably have a digital assistant on your mobile phone, and if you go to the home of someone even mildly tech-savvy, you may see a device awaiting commands via a wake word such the device’s name or, for the Google Assistant, the phrase “OK, Google”.

Homebrew versions

Understanding the maker need to ‘put tech into stuff’ and upgrade everyday objects into everyday objects 2.0, the creators of these virtual assistants have allowed access for developers to run their software on devices such as the Raspberry Pi. This means that your common-or-garden homemade robot can now be controlled via voice, and your shed-built home automation system can have easy-to-use internet connectivity via a reliable, multi-device platform.

Andrew’s Google Assistant build

Andrew gives a peerless explanation of how the Google Assistant works:

There’s Google’s Cloud. You log into Google’s Cloud and you do a bunch of cloud configuration cloud stuff. And then on the Raspberry Pi you install some Python software and you do a bunch of configuration. And then the cloud and the Pi talk the clouds kitten rainbow protocol and then you get a Google AI assistant.

It all makes perfect sense. Though for more extra detail, you could always head directly to Google.

Andrew Jones Raspberry Pi OK Google Assistant

I couldn’t have explained it better myself

Andrew decided to take his Google Assistant-enabled Raspberry Pi and create a new body for it. One that was more aesthetically pleasing than the standard Pi-inna-box. After wiring his build and cannibalising some speakers and a microphone, he created a sleek, wooden body that would sit quite comfortably in any Bang & Olufsen shop window.

Find the entire build tutorial on Instructables.

Make your own

It’s more straightforward than Andrew’s explanation suggests, we promise! And with an array of useful resources online, you should be able to incorporate your choice of virtual assistants into your build.

There’s The Raspberry Pi Guy’s tutorial on setting up Amazon Alexa on the Raspberry Pi. If you’re looking to use Siri on your Pi, YouTube has a plethora of tutorials waiting for you. And lastly, check out Microsoft’s site for using Cortana on the Pi!

If you’re looking for more information on Google Assistant, check out issue 57 of The MagPi Magazine, free to download as a PDF. The print edition of this issue came with a free AIY Projects Voice Kit, and you can sign up for The MagPi newsletter to be the first to know about the kit’s availability for purchase.

The post OK Google, be aesthetically pleasing appeared first on Raspberry Pi.

Launch – Hello Amazon Macie: Automatically Discover, Classify, and Secure Content at Scale

Post Syndicated from Tara Walker original https://aws.amazon.com/blogs/aws/launch-amazon-macie-securing-your-s3-buckets/

When Jeff and I heard about this service, we both were curious on the meaning of the name Macie. Of course, Jeff being a great researcher looked up the name Macie and found that the name Macie has two meanings. It has both French and English (UK) based origin, it is typically a girl name, has various meanings. The first meaning of Macie that was found, said that that name meant “weapon”.  The second meaning noted the name was representative of a person that is bold, sporty, and sweet. In a way, these definitions are appropriate, as today I am happy to announce that we are launching  Amazon Macie, a new security service that uses machine learning to help identify and protect sensitive data stored in AWS from breaches, data leaks, and unauthorized access with Amazon Simple Storage Service (S3) being the initial data store. Therefore, I can imagine that Amazon Macie could be described as a bold, weapon for AWS customers providing a sweet service with a sporty user interface that helps to protects against malicious access of your data at rest. Whew, that was a mouthful, but I unbelievably got all the Macie descriptions out in a single sentence! Nevertheless, I am a thrilled to share with you the power of the new Amazon Macie service.

Amazon Macie is a service powered by machine learning that can automatically discover and classify your data stored in Amazon S3. But Macie doesn’t stop there, once your data has been classified by Macie, it assigns each data item a business value, and then continuously monitors the data in order to detect any suspicious activity based upon access patterns. Key features of the Macie service include:

  • Data Security Automation: analyzes, classifies, and processes data to understand the historical patterns, user authentications to data, data access locations, and times of access.
  • Data Security & Monitoring: actively monitors usage log data for anomaly detected along with automatic resolution of reported issues through CloudWatch Events and Lambda
  • Data Visibility for Proactive Loss prevention: Provides management visibility into details of storage data while providing immediate protection without the need for manual customer input
  • Data Research and Reporting: allows administrative configuration for reporting and alert management requirements

How does Amazon Macie accomplish this you ask? 

Using machine learning algorithms for natural language processing (NLP), Macie can automate the classification of data in your S3 buckets. In addition, Amazon Macie takes advantage of predictive analytics algorithms enabling data access patterns to be dynamically analyzed. Learnings are then used to inform and to alert you on possible suspicious behavior. Macie also runs an engine specifically to detect common sources of personally identifiable information (PII), or sensitive personal information (SP).  Macie takes advantage of AWS CloudTrail and continuously checks Cloudtrail events for PUT requests in S3 buckets and automatically classify new objects in almost real time.

While Macie is a powerful tool to use for security and data protection in the AWS cloud, it also can aid you with governance, compliance requirements, and/or audit standards.  Many of you may already be aware of the EU’s most stringent privacy regulation to date – The General Protection Data Regulation (GDPR), which becomes enforceable on May 25, 2018. As Amazon Macie recognizes personally identifiable information (PII) and provides customers with dashboards and alerts, it will enable customers to comply with GDPR regulations around encryption and pseudonymization of data. When combined with Lambda queries, Macie becomes a powerful tool to help remediate GDPR concerns.

Tour of the Amazon Macie Service

Let’s look a tour of the service and look at Amazon Macie up close and personal.

First, I will log onto the Macie console and start the process of setting up Macie so that I can start to my data classification and protection by clicking the Get Started button.


As you can see, to enable the Amazon Macie service, I must have the appropriate IAM roles created for the service, and additionally I will need to have AWS CloudTrail enabled in my account.

I will create these roles and turn on the AWS CloudTrail service in my account. To make things easier for you to setup Macie, you can take advantage of sample template for CloudFormation provided in the Macie User Guide that will set up required IAM roles and policies for you, you then would only need to setup a trail as noted in the CloudTrail documentation.

If you have multiple AWS accounts, you should note that the account you use to enable the Macie service will be noted as the master account, you can integrate other accounts with the Macie service but they will have the member account designation. Users from member accounts will need to use an IAM role to federate access to the master account in order access the Macie console.

Now that my IAM roles are created and CloudTrail is enabled, I will click the Enable Macie button to start Macie’s data monitoring and protection.


Once Macie is finished starting the service in your account, you will be brought to the service main screen and any existing alerts in your account will be presented to you. Since I have just started the service, I currently have no existing alerts at this time.


Considering we are doing a tour of the Macie service, I will now integrate some of my S3 buckets with Macie. However, you do not have to specify any S3 buckets for Macie to start monitoring since the service already uses the AWS CloudTrail Management API analyze and process information. With this tour of Macie, I have decided to monitor some object level API events in from certain buckets in CloudTrail.

In order to integrate with S3, I will go to the Integrations tab of the Macie console.  Once on the Integrations tab, I will see two options: Accounts and Services. The Account option is used to integrate member accounts with Macie and to set your data retention policy. Since I want to integrate specific S3 buckets with Macie, I’ll click the Services option go to the Services tab.


When I integrate Macie with the S3 service, a trail and a S3 bucket will be created to store logs about S3 data events. To get started, I will use the Select an account drop down to choose an account.  Once my account is selected, the services available for integration are presented. I’ll select the Amazon S3 service by clicking the Add button.

Now I can select the buckets that I want Macie to analyze, selecting the Review and Save button takes me to a screen which I confirm that I desire object level logging by clicking Save button.

4
Next, on our Macie tour, let’s look at how we can customize data classification with Macie.

As we discussed, Macie will automatically monitor and classify your data. Once Macie identifies your data it will classify your data objects by file and content type. Macie will also use a support vector machine (SVM) classifier to classify the content within S3 objects in addition to the metadata of the file. In deep learning/machine learning fields of study, support vector machines are supervised learning models, which have learning algorithms used for classification and regression analysis of data. Macie trained the SVM classifier by using a data of varying content types optimized to support accurate detection of data content even including the source code you may write.

Macie will assign only one content type per data object or file, however, you have the ability to enable or disable content type and file extensions in order to include or exclude them from the Macie service classifying these objects. Once Macie classifies the data, it will assign risk level of the object between 1 and 10 with 10 being the highest risk and 1 being the lowest data risk level.

To customize the classification of our data with Macie, I’ll go to the Settings Tab. I am now presented with the choices available to enable or disable the Macie classifications settings.


For an example during our tour of Macie, I will choose File extension. When presented with the list of file extensions that Macie tracks and uses for classifications.

As a test, I’ll edit the apk file extension for Android application install file, and disable monitoring of this file by selecting No – disabled from the dropdown and clicking the Save button. Of course, later I will turn this back on since I want to keep my entire collection of data files safe including my Android development binaries.


One last thing I want to note about data classification using Macie is that the service provides visibility in how you data object are being classified and highlights data assets that you have stored regarding how critical or important the information for compliance, for your personal data and for your business.

Now that we have explored the data that Macie classifies and monitors, the last stop on our service tour is the Macie dashboard.

 

The Macie Dashboard provides us with a complete picture of all of the data and activity that has been gathered as Macie monitors and classifies our data. The dashboard displays Metrics and Views grouped by categories to provide different visual perspectives of your data. Within these dashboard screens, you also you can go from a metric perspective directly to the Research tab to build and run queries based on the metric. These queries can be used to set up customized alerts for notification of any possible security issues or problems. We won’t have an opportunity to tour the Research or Alerts tab, but you can find out more information about these features in the Macie user guide.

Turning back to the Dashboard, there are so many great resources in the Macie Dashboard that we will not be able to stop at each view, metric, and feature during our tour, so let me give you an overview of all the features of the dashboard that you can take advantage of using.

Dashboard Metrics – monitored data grouped by the following categories:

  • High-risk S3 objects: data objects with risk levels of 8 through 10.
  • Total event occurrences: – total count of all event occurrences since Macie was enabled
  • Total user sessions – 5-minute snapshot of CloudTrail data

Dashboard Views – views to display various points of the monitored data and activity:

  • S3 objects for a selected time range
  • S3 objects
  • S3 objects by personally identifiable information (PII)
  • S3 objects by ACL
  • CloudTrail events and associated users
  • CloudTrail errors and associated users
  • Activity location
  • AWS CLoudTrail events
  • Activity ISPs
  • AWS CloudTrail user identity types

Summary

Well, that concludes our tour of the new and exciting Amazon Macie service. Amazon Macie is a sensational new service that uses the power of machine learning and deep learning to aid you in securing, identifying, and protecting your data stored in Amazon S3. Using natural language processing (NLP) to automate data classification, Amazon Macie enables you to easily get started with high accuracy classification and immediate protection of your data by simply enabling the service.  The interactive dashboards give visibility to the where, what, who, and when of your information allowing you to proactively analyze massive streams of data, data accesses, and API calls in your environment. Learn more about Amazon Macie by visiting the product page or the documentation in the Amazon Macie user guide.

– Tara

New Premier League Blocking Disrupts Pirate IPTV Providers

Post Syndicated from Andy original https://torrentfreak.com/new-premier-league-blocking-disrupts-pirate-iptv-providers-170814/

Top tier football in the UK is handled by the English Premier League (EPL) and broadcasting partners Sky and BT Sport. All face considerable problems with Internet piracy, through free web or Kodi-based streaming and premium IPTV feeds.

To mitigate the threat, earlier this year the Premier League obtained a unique High Court injunction which required ISPs such as Sky, BT, and Virgin to block ‘pirate’ football streams in real-time.

Although the success of the program was initially up for debate, the EPL reported it was able to block 5,000 server IP addresses that were streaming its content. When that temporary injunction ran out, the EPL went back to court for a new one, valid for the season that began this past weekend. There are signs the EPL may have upped its game.

As soon as the matches began on Saturday, issues were reported at several of the more prominent IPTV providers. Within minutes of the match streams going live, subscribers to affected services were met with black screens, causing anger and frustration. While some clearly knew that action was on the cards, relatively few had an effective plan in place.

One provider, which targets subscribers in the UK, scrambled to obtain new domain names, thinking that the existing domains had been placed on some kind of Premier League blacklist. While that may have indeed been the case, making a service more obscure in that sense was never going to outwit the systems deployed by the anti-piracy outfits involved.

Indeed, the provider in question was subjected to much chaos over both Saturday and Sunday, since it’s clear that large numbers of subscribers had absolutely no idea what was going on. Even if they understood that the EPL was blocking, the change of domain flat-footed the rest. The subsequent customer service chaos was not a pretty sight but would’ve been a pleasure for the EPL to behold.

An interesting side effect of this EPL action is that even if IPTV subscribers don’t care about football, many were affected this past weekend anyway.

TF is aware of at least three services (there are probably many more) that couldn’t service their UK customers with any other channels whatsoever while the Premier League games were being aired. This suggests that the IP addresses hit by the EPL and blocked by local ISPs belonged to the same servers carrying the rest of the content offered by the IPTV providers.

When the High Court handed down its original injunction it accepted that some non-Premier League content could be blocked at the same time but since that “consists almost exclusively of [infringing] commercial broadcast content such as other sports, films, and television programs,” there was little concern over collateral damage.

So the big question now is what can IPTV providers and/or subscribers do to tackle the threat?

The first interesting thing to note is not all of the big providers were affected this past weekend, so for many customers the matches passed by as normal. It isn’t clear whether EPL simply didn’t have all of the providers on the list or whether steps were taken to mitigate the threat, but that was certainly the case in a handful of cases.

Information passed to TF shows that at least a small number of providers were not only waiting for the EPL action but actually had a backup plan in place. This appears to have resulted in a minimum of disruption for their customers, something that will prove of interest to the many frustrated subscribers looking for a new service this morning.

While the past few days have been somewhat chaotic, other issues have been muddying the waters somewhat.

TF has learned that at least two, maybe three suppliers, were subjected to DDoS attacks around the time the matches were due to air. It seems unlikely that the EPL has been given permission to carry out such an attack but since the High Court injunction is secret in every way that describes its anti-piracy methods, that will remain a suspicion. In the meantime, rival IPTV services remain possible suspects.

Also, a major IPTV stream ‘wholesaler’ is reported to have had technical issues on Saturday, which affected its ability to serve lower-tier providers. Whether that was also linked to the Premier League action is unknown and TF couldn’t find any source willing to talk about the provider in any detail.

So, sports fans who rely on IPTV for their fix are wondering how things will pan out later this week. If this last weekend is anything to go by, disruption is guaranteed, but it will be less of a surprise given the problems of the last few days. While some don’t foresee huge problems, several providers are already advising customers that VPNs will be necessary.

An IPTV provider suggesting the use of VPNs

While a VPN will indeed solve the problem in most cases, for many subscribers that will amount to an additional expense, not to mention more time spent learning about VPNs, what they can do, and how they can be setup on the hardware they’re using for IPTV.

For users on Android devices running IPTV apps or Kodi-type setups, VPNs are both easy to install and use. However, Mag Box STB users cannot run a VPN directly on the device, meaning that they’ll need either a home router that can run a VPN or a smaller ‘travel’ type router with OpenVPN capabilities to use as a go-between.

Either way, costs are beginning to creep up, if IPTV providers can’t deal with the EPL’s blocking efforts. That makes the new cheaper football packages offered by various providers that little bit more attractive. But that was probably the plan all along.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

New – AWS SAM Local (Beta) – Build and Test Serverless Applications Locally

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/new-aws-sam-local-beta-build-and-test-serverless-applications-locally/

Today we’re releasing a beta of a new tool, SAM Local, that makes it easy to build and test your serverless applications locally. In this post we’ll use SAM local to build, debug, and deploy a quick application that allows us to vote on tabs or spaces by curling an endpoint. AWS introduced Serverless Application Model (SAM) last year to make it easier for developers to deploy serverless applications. If you’re not already familiar with SAM my colleague Orr wrote a great post on how to use SAM that you can read in about 5 minutes. At it’s core, SAM is a powerful open source specification built on AWS CloudFormation that makes it easy to keep your serverless infrastructure as code – and they have the cutest mascot.

SAM Local takes all the good parts of SAM and brings them to your local machine.

There are a couple of ways to install SAM Local but the easiest is through NPM. A quick npm install -g aws-sam-local should get us going but if you want the latest version you can always install straight from the source: go get github.com/awslabs/aws-sam-local (this will create a binary named aws-sam-local, not sam).

I like to vote on things so let’s write a quick SAM application to vote on Spaces versus Tabs. We’ll use a very simple, but powerful, architecture of API Gateway fronting a Lambda function and we’ll store our results in DynamoDB. In the end a user should be able to curl our API curl https://SOMEURL/ -d '{"vote": "spaces"}' and get back the number of votes.

Let’s start by writing a simple SAM template.yaml:

AWSTemplateFormatVersion : '2010-09-09'
Transform: AWS::Serverless-2016-10-31
Resources:
  VotesTable:
    Type: "AWS::Serverless::SimpleTable"
  VoteSpacesTabs:
    Type: "AWS::Serverless::Function"
    Properties:
      Runtime: python3.6
      Handler: lambda_function.lambda_handler
      Policies: AmazonDynamoDBFullAccess
      Environment:
        Variables:
          TABLE_NAME: !Ref VotesTable
      Events:
        Vote:
          Type: Api
          Properties:
            Path: /
            Method: post

So we create a [dynamo_i] table that we expose to our Lambda function through an environment variable called TABLE_NAME.

To test that this template is valid I’ll go ahead and call sam validate to make sure I haven’t fat-fingered anything. It returns Valid! so let’s go ahead and get to work on our Lambda function.

import os
import os
import json
import boto3
votes_table = boto3.resource('dynamodb').Table(os.getenv('TABLE_NAME'))

def lambda_handler(event, context):
    print(event)
    if event['httpMethod'] == 'GET':
        resp = votes_table.scan()
        return {'body': json.dumps({item['id']: int(item['votes']) for item in resp['Items']})}
    elif event['httpMethod'] == 'POST':
        try:
            body = json.loads(event['body'])
        except:
            return {'statusCode': 400, 'body': 'malformed json input'}
        if 'vote' not in body:
            return {'statusCode': 400, 'body': 'missing vote in request body'}
        if body['vote'] not in ['spaces', 'tabs']:
            return {'statusCode': 400, 'body': 'vote value must be "spaces" or "tabs"'}

        resp = votes_table.update_item(
            Key={'id': body['vote']},
            UpdateExpression='ADD votes :incr',
            ExpressionAttributeValues={':incr': 1},
            ReturnValues='ALL_NEW'
        )
        return {'body': "{} now has {} votes".format(body['vote'], resp['Attributes']['votes'])}

So let’s test this locally. I’ll need to create a real DynamoDB database to talk to and I’ll need to provide the name of that database through the enviornment variable TABLE_NAME. I could do that with an env.json file or I can just pass it on the command line. First, I can call:
$ echo '{"httpMethod": "POST", "body": "{\"vote\": \"spaces\"}"}' |\
TABLE_NAME="vote-spaces-tabs" sam local invoke "VoteSpacesTabs"
to test the Lambda – it returns the number of votes for spaces so theoritically everything is working. Typing all of that out is a pain so I could generate a sample event with sam local generate-event api and pass that in to the local invocation. Far easier than all of that is just running our API locally. Let’s do that: sam local start-api. Now I can curl my local endpoints to test everything out.
I’ll run the command: $ curl -d '{"vote": "tabs"}' http://127.0.0.1:3000/ and it returns: “tabs now has 12 votes”. Now, of course I did not write this function perfectly on my first try. I edited and saved several times. One of the benefits of hot-reloading is that as I change the function I don’t have to do any additional work to test the new function. This makes iterative development vastly easier.

Let’s say we don’t want to deal with accessing a real DynamoDB database over the network though. What are our options? Well we can download DynamoDB Local and launch it with java -Djava.library.path=./DynamoDBLocal_lib -jar DynamoDBLocal.jar -sharedDb. Then we can have our Lambda function use the AWS_SAM_LOCAL environment variable to make some decisions about how to behave. Let’s modify our function a bit:

import os
import json
import boto3
if os.getenv("AWS_SAM_LOCAL"):
    votes_table = boto3.resource(
        'dynamodb',
        endpoint_url="http://docker.for.mac.localhost:8000/"
    ).Table("spaces-tabs-votes")
else:
    votes_table = boto3.resource('dynamodb').Table(os.getenv('TABLE_NAME'))

Now we’re using a local endpoint to connect to our local database which makes working without wifi a little easier.

SAM local even supports interactive debugging! In Java and Node.js I can just pass the -d flag and a port to immediately enable the debugger. For Python I could use a library like import epdb; epdb.serve() and connect that way. Then we can call sam local invoke -d 8080 "VoteSpacesTabs" and our function will pause execution waiting for you to step through with the debugger.

Alright, I think we’ve got everything working so let’s deploy this!

First I’ll call the sam package command which is just an alias for aws cloudformation package and then I’ll use the result of that command to sam deploy.

$ sam package --template-file template.yaml --s3-bucket MYAWESOMEBUCKET --output-template-file package.yaml
Uploading to 144e47a4a08f8338faae894afe7563c3  90570 / 90570.0  (100.00%)
Successfully packaged artifacts and wrote output template to file package.yaml.
Execute the following command to deploy the packaged template
aws cloudformation deploy --template-file package.yaml --stack-name 
$ sam deploy --template-file package.yaml --stack-name VoteForSpaces --capabilities CAPABILITY_IAM
Waiting for changeset to be created..
Waiting for stack create/update to complete
Successfully created/updated stack - VoteForSpaces

Which brings us to our API:
.

I’m going to hop over into the production stage and add some rate limiting in case you guys start voting a lot – but otherwise we’ve taken our local work and deployed it to the cloud without much effort at all. I always enjoy it when things work on the first deploy!

You can vote now and watch the results live! http://spaces-or-tabs.s3-website-us-east-1.amazonaws.com/

We hope that SAM Local makes it easier for you to test, debug, and deploy your serverless apps. We have a CONTRIBUTING.md guide and we welcome pull requests. Please tweet at us to let us know what cool things you build. You can see our What’s New post here and the documentation is live here.

– Randall

Bassi: Dev v Ops

Post Syndicated from corbet original https://lwn.net/Articles/730630/rss

Emmanuele Bassi writes about the
mismatch between the traditional distribution packaging model and what
the world seems to actually want. “The more I think about it, the less I understand how that ever worked in the first place. It is not a mystery, though, why it’s a dying model.

When I say that ‘nobody develops applications like the Linux distributions
encourages and prefers’ I’m not kidding around: Windows, macOS, iOS,
Electron, and Android application developers are heavily based on the
concept of a core set of OS services; a parallel installable blocks of
system dependencies shipped and retired by the OS vendor; and a bundling
system that allows application developers to provide their own
dependencies, and control them.”

Turning an Amazon Echo into an Eavesdropping Device

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2017/08/turning_an_amaz.html

For once, the real story isn’t as bad as it seems. A researcher has figured out how to install malware onto an Echo that causes it to stream audio back to a remote controller, but:

The technique requires gaining physical access to the target Echo, and it works only on devices sold before 2017. But there’s no software fix for older units, Barnes warns, and the attack can be performed without leaving any sign of hardware intrusion.

The way to implement this attack is by intercepting the Echo before it arrives at the target location. But if you can do that, there are a lot of other things you can do. So while this is a vulnerability that needs to be fixed — and seems to have inadvertently been fixed — it’s not a cause for alarm.

Video playback on freely-arranged screens with info-beamer

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/info-beamer/

When the creator of the digital signage software info-beamer, Florian Wesch, shared this project on Reddit, I don’t think he was prepared for the excited reaction of the community. Florian’s post, which by now has thousands of upvotes, showcased the power of info-beamer. Not only can the software display a video via multiple Raspberry Pis, it also automatically rejigs the output to match the size and angle of the Pis’ monitors.

info-beamer raspberry pi

Wait…what?

I know, right? We’ve seen many video-based Raspberry Pi projects, but this is definitely one of the most impressive ones. While those of us with a creative streak were imagining cool visual arts installations using monitors and old televisions of various sizes, the more technically-minded puzzled over how Florian pulled this off.

It’s obvious that info-beamer has manifold potential uses. But we had absolutely zero understanding of how it works!

How does info-beamer do this?

Lucky for us, Florian returned to Reddit a few days later with a how-to video, explaining in layman’s terms how you too can get a video to play on a multi-screen, multi-Pi setup.

Automatic video wall configuration with info-beamer hosted

This is an exciting new feature I’ve made available for the info-beamer hosted digital signage system: You can create a video wall consisting of freely arranged screens in seconds. The screens don’t even have to be planar. Just rotate and place them as you like.

First you’ll need to set up info-beamer, which will allow you to introduce multiple Raspberry Pis, and their attached monitors, into a joint network. To make the software work, there’s some Python code you have to write yourself, but hands-on tutorials and example code exist to make this fairly easy, even if you have little experience in Python.

info-beamer raspberry pi

As you can see in Florian’s video, info-beamer assigns each monitor its own, unique section of video. Taking a photo of the monitors and uploading it to a site provides enough information for the software to play a movie trailer split across multiple screens.

info-beamer raspberry pi

A step that’s missing in the video, but that Florian described on Reddit, is how to configure the screens via a drag-and-drop interface so that the software recognizes them. Once this is done, your video display is good to go.

For more information about info-beamer check out the website, and follow the official Twitter account for updates.

Using Raspberry Pi in video-based projects

Since it has an HDMI port, connecting your Raspberry Pi to any compatible monitor, including your television, is an easy task. And with a little tweaking and soldering you can even connect your Pi to that ageing SCART TV/Video combo you might have in the loft.

As I said earlier, there’s an abundance of Pi-powered video-based projects. Many digital art installations, and even commercial media devices, rely on the Raspberry Pi because of its low cost, small size, and high-quality multimedia capabilities.

Have you used a Raspberry Pi in a video-playback project? Share it with us below – we’d love to see it!

The post Video playback on freely-arranged screens with info-beamer appeared first on Raspberry Pi.

ESET Tries to Scare People Away From Using Torrents

Post Syndicated from Andy original https://torrentfreak.com/eset-tries-to-scare-people-away-from-using-torrents-170805/

Any company in the security game can be expected to play up threats among its customer base in order to get sales.

Sellers of CCTV equipment, for example, would have us believe that criminals don’t want to be photographed and will often go elsewhere in the face of that. Car alarm companies warn us that since X thousand cars are stolen every minute, an expensive Immobilizer is an anti-theft must.

Of course, they’re absolutely right to point these things out. People want to know about these offline risks since they affect our quality of life. The same can be said of those that occur in the online world too.

We ARE all at risk of horrible malware that will trash our computers and steal our banking information so we should all be running adequate protection. That being said, how many times do our anti-virus programs actually trap a piece of nasty-ware in a year? Once? Twice? Ten times? Almost never?

The truth is we all need to be informed but it should be done in a measured way. That’s why an article just published by security firm ESET on the subject of torrents strikes a couple of bad chords, particularly with people who like torrents. It’s titled “Why you should view torrents as a threat” and predictably proceeds to outline why.

“Despite their popularity among users, torrents are very risky ‘business’,” it begins.

“Apart from the obvious legal trouble you could face for violating the copyright of musicians, filmmakers or software developers, there are security issues linked to downloading them that could put you or your computer in the crosshairs of the black hats.”

Aside from the use of the phrase “very risky” (‘some risk’ is a better description), there’s probably very little to complain about in this opening shot. However, things soon go downhill.

“Merely downloading the newest version of BitTorrent clients – software necessary for any user who wants to download or seed files from this ‘ecosystem’ – could infect your machine and irreversibly damage your files,” ESET writes.

Following that scary statement, some readers will have already vowed never to use a torrent again and moved on without reading any more, but the details are really important.

To support its claim, ESET points to two incidents in 2016 (which to its great credit the company actually discovered) which involved the Transmission torrent client. Both involved deliberate third-party infection and in the latter hackers attacked Transmission’s servers and embedded malware in its OSX client before distribution to the public.

No doubt these were both miserable incidents (to which the Transmission team quickly responded) but to characterize this as a torrent client problem seems somewhat unfair.

People intent on spreading viruses and malware do not discriminate and will happily infect ANY piece of computer software they can. Sadly, many non-technical people reading the ESET post won’t read beyond the claim that installing torrent clients can “infect your machine and irreversibly damage your files.”

That’s a huge disservice to the hundreds of millions of torrent client installations that have taken place over a decade and a half and were absolutely trouble free. On a similar basis, we could argue that installing Windows is the main initial problem for people getting viruses from the Internet. It’s true but it’s also not the full picture.

Finally, the piece goes on to detail other incidents over the years where torrents have been found to contain malware. The several cases highlighted by ESET are both real and pretty unpleasant for victims but the important thing to note here is torrent users are no different to any other online user, no matter how they use the Internet.

People who download files from the Internet, from ALL untrusted sources, are putting themselves at risk of getting a virus or other malware. Whether that content is obtained from a website or a P2P network, the risks are ever-present and only a foolish person would do so without decent security software (such as ESET’s) protecting them.

The take home point here is to be aware of security risks and put them into perspective. It’s hard to put a percentage on these things but of the hundreds of millions of torrent and torrent client downloads that have taken place since their inception 15 years ago, the overwhelming majority have been absolutely fine.

Security situations do arise and we need to be aware of them, but presenting things in a way that spreads unnecessary concern in a particular sector isn’t necessary to sell products.

The AV-TEST Institute registers around 390,000 new malicious programs every day that don’t involve torrents, plenty for any anti-virus firm to deal with.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

Turbocharge your Apache Hive queries on Amazon EMR using LLAP

Post Syndicated from Jigar Mistry original https://aws.amazon.com/blogs/big-data/turbocharge-your-apache-hive-queries-on-amazon-emr-using-llap/

Apache Hive is one of the most popular tools for analyzing large datasets stored in a Hadoop cluster using SQL. Data analysts and scientists use Hive to query, summarize, explore, and analyze big data.

With the introduction of Hive LLAP (Low Latency Analytical Processing), the notion of Hive being just a batch processing tool has changed. LLAP uses long-lived daemons with intelligent in-memory caching to circumvent batch-oriented latency and provide sub-second query response times.

This post provides an overview of Hive LLAP, including its architecture and common use cases for boosting query performance. You will learn how to install and configure Hive LLAP on an Amazon EMR cluster and run queries on LLAP daemons.

What is Hive LLAP?

Hive LLAP was introduced in Apache Hive 2.0, which provides very fast processing of queries. It uses persistent daemons that are deployed on a Hadoop YARN cluster using Apache Slider. These daemons are long-running and provide functionality such as I/O with DataNode, in-memory caching, query processing, and fine-grained access control. And since the daemons are always running in the cluster, it saves substantial overhead of launching new YARN containers for every new Hive session, thereby avoiding long startup times.

When Hive is configured in hybrid execution mode, small and short queries execute directly on LLAP daemons. Heavy lifting (like large shuffles in the reduce stage) is performed in YARN containers that belong to the application. Resources (CPU, memory, etc.) are obtained in a traditional fashion using YARN. After the resources are obtained, the execution engine can decide which resources are to be allocated to LLAP, or it can launch Apache Tez processors in separate YARN containers. You can also configure Hive to run all the processing workloads on LLAP daemons for querying small datasets at lightning fast speeds.

LLAP daemons are launched under YARN management to ensure that the nodes don’t get overloaded with the compute resources of these daemons. You can use scheduling queues to make sure that there is enough compute capacity for other YARN applications to run.

Why use Hive LLAP?

With many options available in the market (Presto, Spark SQL, etc.) for doing interactive SQL  over data that is stored in Amazon S3 and HDFS, there are several reasons why using Hive and LLAP might be a good choice:

  • For those who are heavily invested in the Hive ecosystem and have external BI tools that connect to Hive over JDBC/ODBC connections, LLAP plugs in to their existing architecture without a steep learning curve.
  • It’s compatible with existing Hive SQL and other Hive tools, like HiveServer2, and JDBC drivers for Hive.
  • It has native support for security features with authentication and authorization (SQL standards-based authorization) using HiveServer2.
  • LLAP daemons are aware about of the columns and records that are being processed which enables you to enforce fine-grained access control.
  • It can use Hive’s vectorization capabilities to speed up queries, and Hive has better support for Parquet file format when vectorization is enabled.
  • It can take advantage of a number of Hive optimizations like merging multiple small files for query results, automatically determining the number of reducers for joins and groupbys, etc.
  • It’s optional and modular so it can be turned on or off depending on the compute and resource requirements of the cluster. This lets you to run other YARN applications concurrently without reserving a cluster specifically for LLAP.

How do you install Hive LLAP in Amazon EMR?

To install and configure LLAP on an EMR cluster, use the following bootstrap action (BA):

s3://aws-bigdata-blog/artifacts/Turbocharge_Apache_Hive_on_EMR/configure-Hive-LLAP.sh

This BA downloads and installs Apache Slider on the cluster and configures LLAP so that it works with EMR Hive. For LLAP to work, the EMR cluster must have Hive, Tez, and Apache Zookeeper installed.

You can pass the following arguments to the BA.

Argument Definition Default value
--instances Number of instances of LLAP daemon Number of core/task nodes of the cluster
--cache Cache size per instance 20% of physical memory of the node
--executors Number of executors per instance Number of CPU cores of the node
--iothreads Number of IO threads per instance Number of CPU cores of the node
--size Container size per instance 50% of physical memory of the node
--xmx Working memory size 50% of container size
--log-level Log levels for the LLAP instance INFO

LLAP example

This section describes how you can try the faster Hive queries with LLAP using the TPC-DS testbench for Hive on Amazon EMR.

Use the following AWS command line interface (AWS CLI) command to launch a 1+3 nodes m4.xlarge EMR 5.6.0 cluster with the bootstrap action to install LLAP:

aws emr create-cluster --release-label emr-5.6.0 \
--applications Name=Hadoop Name=Hive Name=Hue Name=ZooKeeper Name=Tez \
--bootstrap-actions '[{"Path":"s3://aws-bigdata-blog/artifacts/Turbocharge_Apache_Hive_on_EMR/configure-Hive-LLAP.sh","Name":"Custom action"}]' \ 
--ec2-attributes '{"KeyName":"<YOUR-KEY-PAIR>","InstanceProfile":"EMR_EC2_DefaultRole","SubnetId":"subnet-xxxxxxxx","EmrManagedSlaveSecurityGroup":"sg-xxxxxxxx","EmrManagedMasterSecurityGroup":"sg-xxxxxxxx"}' 
--service-role EMR_DefaultRole \
--enable-debugging \
--log-uri 's3n://<YOUR-BUCKET/' --name 'test-hive-llap' \
--instance-groups '[{"InstanceCount":1,"EbsConfiguration":{"EbsBlockDeviceConfigs":[{"VolumeSpecification":{"SizeInGB":32,"VolumeType":"gp2"},"VolumesPerInstance":1}],"EbsOptimized":true},"InstanceGroupType":"MASTER","InstanceType":"m4.xlarge","Name":"Master - 1"},{"InstanceCount":3,"EbsConfiguration":{"EbsBlockDeviceConfigs":[{"VolumeSpecification":{"SizeInGB":32,"VolumeType":"gp2"},"VolumesPerInstance":1}],"EbsOptimized":true},"InstanceGroupType":"CORE","InstanceType":"m4.xlarge","Name":"Core - 2"}]' 
--region us-east-1

After the cluster is launched, log in to the master node using SSH, and do the following:

  1. Open the hive-tpcds folder:
    cd /home/hadoop/hive-tpcds/
  2. Start Hive CLI using the testbench configuration, create the required tables, and run the sample query:

    hive –i testbench.settings
    hive> source create_tables.sql;
    hive> source query55.sql;
    This sample query runs on a 40 GB dataset that is stored on Amazon S3. The dataset is generated using the data generation tool in the TPC-DS testbench for Hive.It results in output like the following:
  3. This screenshot shows that the query finished in about 47 seconds for LLAP mode. Now, to compare this to the execution time without LLAP, you can run the same workload using only Tez containers:
    hive> set hive.llap.execution.mode=none;
    hive> source query55.sql;

    This query finished in about 80 seconds.

The difference in query execution time is almost 1.7 times when using just YARN containers in contrast to running the query on LLAP daemons. And with every rerun of the query, you notice that the execution time substantially decreases by the virtue of in-memory caching by LLAP daemons.

Conclusion

In this post, I introduced Hive LLAP as a way to boost Hive query performance. I discussed its architecture and described several use cases for the component. I showed how you can install and configure Hive LLAP on an Amazon EMR cluster and how you can run queries on LLAP daemons.

If you have questions about using Hive LLAP on Amazon EMR or would like to share your use cases, please leave a comment below.

Additional Reading

Learn how to to automatically partition Hive external tables with AWS.

About the Author

Jigar Mistry is a Hadoop Systems Engineer with Amazon Web Services. He works with customers to provide them architectural guidance and technical support for processing large datasets in the cloud using open-source applications. In his spare time, he enjoys going for camping and exploring different restaurants in the Seattle area.

 

 

 

 

Updates to GPIO Zero, the physical computing API

Post Syndicated from Ben Nuttall original https://www.raspberrypi.org/blog/gpio-zero-update/

GPIO Zero v1.4 is out now! It comes with a set of new features, including a handy pinout command line tool. To start using this newest version of the API, update your Raspbian OS now:

sudo apt update && sudo apt upgrade

Some of the things we’ve added will make it easier for you try your hand on different programming styles. In doing so you’ll build your coding skills, and will improve as a programmer. As a consequence, you’ll learn to write more complex code, which will enable you to take on advanced electronics builds. And on top of that, you can use the skills you’ll acquire in other computing projects.

GPIO Zero pinout tool

The new pinout tool

Developing GPIO Zero

Nearly two years ago, I started the GPIO Zero project as a simple wrapper around the low-level RPi.GPIO library. I wanted to create a simpler way to control GPIO-connected devices in Python, based on three years’ experience of training teachers, running workshops, and building projects. The idea grew over time, and the more we built for our Python library, the more sophisticated and powerful it became.

One of the great things about Python is that it’s a multi-paradigm programming language. You can write code in a number of different styles, according to your needs. You don’t have to write classes, but you can if you need them. There are functional programming tools available, but beginners get by without them. Importantly, the more advanced features of the language are not a barrier to entry.

Become a more advanced programmer

As a beginner to programming, you usually start by writing procedural programs, in which the flow moves from top to bottom. Then you’ll probably add loops and create your own functions. Your next step might be to start using libraries which introduce new patterns that operate in a different manner to what you’ve written before, for example threaded callbacks (event-driven programming). You might move on to object-oriented programming, extending the functionality of classes provided by other libraries, and starting to write your own classes. Occasionally, you may make use of tools created with functional programming techniques.

Five buttons in different colours

Take control of the buttons in your life

It’s much the same with GPIO Zero: you can start using it very easily, and we’ve made it simple to progress along the learning curve towards more advanced programming techniques. For example, if you want to make a push button control an LED, the easiest way to do this is via procedural programming using a while loop:

from gpiozero import LED, Button

led = LED(17)
button = Button(2)

while True:
    if button.is_pressed:
        led.on()
    else:
        led.off()

But another way to achieve the same thing is to use events:

from gpiozero import LED, Button
from signal import pause

led = LED(17)
button = Button(2)

button.when_pressed = led.on
button.when_released = led.off

pause()

You could even use a declarative approach, and set the LED’s behaviour in a single line:

from gpiozero import LED, Button
from signal import pause

led = LED(17)
button = Button(2)

led.source = button.values

pause()

You will find that using the procedural approach is a great start, but at some point you’ll hit a limit, and will have to try a different approach. The example above can be approach in several programming styles. However, if you’d like to control a wider range of devices or a more complex system, you need to carefully consider which style works best for what you want to achieve. Being able to choose the right programming style for a task is a skill in itself.

Source/values properties

So how does the led.source = button.values thing actually work?

Every GPIO Zero device has a .value property. For example, you can read a button’s state (True or False), and read or set an LED’s state (so led.value = True is the same as led.on()). Since LEDs and buttons operate with the same value set (True and False), you could say led.value = button.value. However, this only sets the LED to match the button once. If you wanted it to always match the button’s state, you’d have to use a while loop. To make things easier, we came up with a way of telling devices they’re connected: we added a .values property to all devices, and a .source to output devices. Now, a loop is no longer necessary, because this will do the job:

led.source = button.values

This is a simple approach to connecting devices using a declarative style of programming. In one single line, we declare that the LED should get its values from the button, i.e. when the button is pressed, the LED should be on. You can even mix the procedural with the declarative style: at one stage of the program, the LED could be set to match the button, while in the next stage it could just be blinking, and finally it might return back to its original state.

These additions are useful for connecting other devices as well. For example, a PWMLED (LED with variable brightness) has a value between 0 and 1, and so does a potentiometer connected via an ADC (analogue-digital converter) such as the MCP3008. The new GPIO Zero update allows you to say led.source = pot.values, and then twist the potentiometer to control the brightness of the LED.

But what if you want to do something more complex, like connect two devices with different value sets or combine multiple inputs?

We provide a set of device source tools, which allow you to process values as they flow from one device to another. They also let you send in artificial values such as random data, and you can even write your own functions to generate values to pass to a device’s source. For example, to control a motor’s speed with a potentiometer, you could use this code:

from gpiozero import Motor, MCP3008
from signal import pause

motor = Motor(20, 21)
pot = MCP3008()

motor.source = pot.values

pause()

This works, but it will only drive the motor forwards. If you wanted the potentiometer to drive it forwards and backwards, you’d use the scaled tool to scale its values to a range of -1 to 1:

from gpiozero import Motor, MCP3008
from gpiozero.tools import scaled
from signal import pause

motor = Motor(20, 21)
pot = MCP3008()

motor.source = scaled(pot.values, -1, 1)

pause()

And to separately control a robot’s left and right motor speeds with two potentiometers, you could do this:

from gpiozero import Robot, MCP3008
from signal import pause

robot = Robot(left=(2, 3), right=(4, 5))
left = MCP3008(0)
right = MCP3008(1)

robot.source = zip(left.values, right.values)

pause()

GPIO Zero and Blue Dot

Martin O’Hanlon created a Python library called Blue Dot which allows you to use your Android device to remotely control things on their Raspberry Pi. The API is very similar to GPIO Zero, and it even incorporates the value/values properties, which means you can hook it up to GPIO devices easily:

from bluedot import BlueDot
from gpiozero import LED
from signal import pause

bd = BlueDot()
led = LED(17)

led.source = bd.values

pause()

We even included a couple of Blue Dot examples in our recipes.

Make a series of binary logic gates using source/values

Read more in this source/values tutorial from The MagPi, and on the source/values documentation page.

Remote GPIO control

GPIO Zero supports multiple low-level GPIO libraries. We use RPi.GPIO by default, but you can choose to use RPIO or pigpio instead. The pigpio library supports remote connections, so you can run GPIO Zero on one Raspberry Pi to control the GPIO pins of another, or run code on a PC (running Windows, Mac, or Linux) to remotely control the pins of a Pi on the same network. You can even control two or more Pis at once!

If you’re using Raspbian on a Raspberry Pi (or a PC running our x86 Raspbian OS), you have everything you need to remotely control GPIO. If you’re on a PC running Windows, Mac, or Linux, you just need to install gpiozero and pigpio using pip. See our guide on configuring remote GPIO.

I road-tested the new pin_factory syntax at the Raspberry Jam @ Pi Towers

There are a number of different ways to use remote pins:

  • Set the default pin factory and remote IP address with environment variables:
$ GPIOZERO_PIN_FACTORY=pigpio PIGPIO_ADDR=192.168.1.2 python3 blink.py
  • Set the default pin factory in your script:
import gpiozero
from gpiozero import LED
from gpiozero.pins.pigpio import PiGPIOFactory

gpiozero.Device.pin_factory = PiGPIOFactory(host='192.168.1.2')

led = LED(17)
  • The pin_factory keyword argument allows you to use multiple Pis in the same script:
from gpiozero import LED
from gpiozero.pins.pigpio import PiGPIOFactory

factory2 = PiGPIOFactory(host='192.168.1.2')
factory3 = PiGPIOFactory(host='192.168.1.3')

local_hat = TrafficHat()
remote_hat2 = TrafficHat(pin_factory=factory2)
remote_hat3 = TrafficHat(pin_factory=factory3)

This is a really powerful feature! For more, read this remote GPIO tutorial in The MagPi, and check out the remote GPIO recipes in our documentation.

GPIO Zero on your PC

GPIO Zero doesn’t have any dependencies, so you can install it on your PC using pip. In addition to the API’s remote GPIO control, you can use its ‘mock’ pin factory on your PC. We originally created the mock pin feature for the GPIO Zero test suite, but we found that it’s really useful to be able to test GPIO Zero code works without running it on real hardware:

$ GPIOZERO_PIN_FACTORY=mock python3
>>> from gpiozero import LED
>>> led = LED(22)
>>> led.blink()
>>> led.value
True
>>> led.value
False

You can even tell pins to change state (e.g. to simulate a button being pressed) by accessing an object’s pin property:

>>> from gpiozero import LED
>>> led = LED(22)
>>> button = Button(23)
>>> led.source = button.values
>>> led.value
False
>>> button.pin.drive_low()
>>> led.value
True

You can also use the pinout command line tool if you set your pin factory to ‘mock’. It gives you a Pi 3 diagram by default, but you can supply a revision code to see information about other Pi models. For example, to use the pinout tool for the original 256MB Model B, just type pinout -r 2.

GPIO Zero documentation and resources

On the API’s website, we provide beginner recipes and advanced recipes, and we have added remote GPIO configuration including PC/Mac/Linux and Pi Zero OTG, and a section of GPIO recipes. There are also new sections on source/values, command-line tools, FAQs, Pi information and library development.

You’ll find plenty of cool projects using GPIO Zero in our learning resources. For example, you could check out the one that introduces physical computing with Python and get stuck in! We even provide a GPIO Zero cheat sheet you can download and print.

There are great GPIO Zero tutorials and projects in The MagPi magazine every month. Moreover, they also publish Simple Electronics with GPIO Zero, a book which collects a series of tutorials useful for building your knowledge of physical computing. And the best thing is, you can download it, and all magazine issues, for free!

Check out the API documentation and read more about what’s new in GPIO Zero on my blog. We have lots planned for the next release. Watch this space.

Get building!

The world of physical computing is at your fingertips! Are you feeling inspired?

If you’ve never tried your hand on physical computing, our Build a robot buggy learning resource is the perfect place to start! It’s your step-by-step guide for building a simple robot controlled with the help of GPIO Zero.

If you have a gee-whizz idea for an electronics project, do share it with us below. And if you’re currently working on a cool build and would like to show us how it’s going, pop a link to it in the comments.

The post Updates to GPIO Zero, the physical computing API appeared first on Raspberry Pi.

timeShift(GrafanaBuzz, 1w) Issue 7

Post Syndicated from Blogs on Grafana Labs Blog original https://grafana.com/blog/2017/08/04/timeshiftgrafanabuzz-1w-issue-7/

Hard to believe it’s already August! This week there were a ton of articles to highlight. It’s really exciting to see how many data aficionados there are out there coming up with new ways to connect Grafana to their data, wherever it may live. In this issue we cover crypto currency visualization, home automation setups and breakdown the installation in a number of environments. Enjoy!

Grafana 4.4.2 Released

Grafana v4.4.2 is now Available for download

This week’s release includes a few updates and some bug fixes.

To see the full changelog, head over to our community site.

From the Blogosphere

Plugins and Dashboards

Last week’s timeShift was packed full of plugin updates, as well as a couple of new ones. This week was a little quieter on the plugin front, but we still have a new data source plugin to announce. It’s easy to install this new plugin via the grafana-cli for an on-prem Grafana instance, or a 1-click install on Hosted Grafana.

NEW PLUGIN

PRTG Data Source – This data source visualizes data from the Paessler PRTG monitoring system. The easy to use query editor included with this plugin gives access to an array of PRTG metadata properties including Status, Message, Active, Tags, Priority, and more. Annotation support to show sensor status messages on graphs.

Install Now

This week’s MVC (Most Valuable Contributor)

This week we highlight a contributor who is going to make everyone waiting for Elasticsearch alerting in Grafana jump for joy!

lvheyang (Cui Wenzheng)
lvheyang submitted a PR for Elasticsearch Alerting that must have been a huge amount of work and is of impressive quality. Thank you very much for your hard work and we look forward to releasing Alerting for Elasticsearch in Grafana 5.0.0.

Tweet of the Week

We scour Twitter each week to find an interesting/beautiful dashboard and show it off! #monitoringLove

Nice @jorgedlcruz! – Making data more beautiful, one boring number at a time 🙂 – Also, I spy one of my favorite panels in there, the Worldmap Panel!

Upcoming Events

We love when people talk about Grafana at meetups and conferences.

Wednesday, August 16, 2017 – 7:30pm | Apprenda HQ


433 River Street, 4th Floor, Troy, NY

Kubernetes focused event! demo from Apprenda and how Kubernetes is used @ GitHub:
Steve Wade, is a Principal Kubernetes Consultant from London and will be providing some fundamental information about the Kubernetes ecosystem as well as overview of its core components. He’ll also talk about some monitoring and alerting best practices learned from working with Kubernetes customers and demo how Prometheus, Grafana and Slack can be used to monitor, visualize and alert on both the Kubernetes platform as well as application workloads.

Aaron Brown, a Site Reliability Engineer at Github, will dive into the ways in which Kubernetes is used within Github to make software development and deployment more efficient.

RSVP

What do you think?

Please tell us how we’re doing. We want to make sure this continues to be a valuable resource for the Grafana community. Submit a comment on this article below, or post something at our community forum. Help us make this better!

Follow us on Twitter, like us on Facebook, and join the Grafana Labs community.

Deploying an NGINX Reverse Proxy Sidecar Container on Amazon ECS

Post Syndicated from Nathan Peck original https://aws.amazon.com/blogs/compute/nginx-reverse-proxy-sidecar-container-on-amazon-ecs/

Reverse proxies are a powerful software architecture primitive for fetching resources from a server on behalf of a client. They serve a number of purposes, from protecting servers from unwanted traffic to offloading some of the heavy lifting of HTTP traffic processing.

This post explains the benefits of a reverse proxy, and explains how to use NGINX and Amazon EC2 Container Service (Amazon ECS) to easily implement and deploy a reverse proxy for your containerized application.

Components

NGINX is a high performance HTTP server that has achieved significant adoption because of its asynchronous event driven architecture. It can serve thousands of concurrent requests with a low memory footprint. This efficiency also makes it ideal as a reverse proxy.

Amazon ECS is a highly scalable, high performance container management service that supports Docker containers. It allows you to run applications easily on a managed cluster of Amazon EC2 instances. Amazon ECS helps you get your application components running on instances according to a specified configuration. It also helps scale out these components across an entire fleet of instances.

Sidecar containers are a common software pattern that has been embraced by engineering organizations. It’s a way to keep server side architecture easier to understand by building with smaller, modular containers that each serve a simple purpose. Just like an application can be powered by multiple microservices, each microservice can also be powered by multiple containers that work together. A sidecar container is simply a way to move part of the core responsibility of a service out into a containerized module that is deployed alongside a core application container.

The following diagram shows how an NGINX reverse proxy sidecar container operates alongside an application server container:

In this architecture, Amazon ECS has deployed two copies of an application stack that is made up of an NGINX reverse proxy side container and an application container. Web traffic from the public goes to an Application Load Balancer, which then distributes the traffic to one of the NGINX reverse proxy sidecars. The NGINX reverse proxy then forwards the request to the application server and returns its response to the client via the load balancer.

Reverse proxy for security

Security is one reason for using a reverse proxy in front of an application container. Any web server that serves resources to the public can expect to receive lots of unwanted traffic every day. Some of this traffic is relatively benign scans by researchers and tools, such as Shodan or nmap:

[18/May/2017:15:10:10 +0000] "GET /YesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScanningForResearchPurposePleaseHaveALookAtTheUserAgentTHXYesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScanningForResearchPurposePleaseHaveALookAtTheUserAgentTHXYesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScanningForResearchPurposePleaseHaveALookAtTheUserAgentTHXYesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScanningForResearchPurposePleaseHaveALookAtTheUserAgentTHXYesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScanningForResearchPurposePleaseHaveALookAtTheUserAgentTHXYesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScanningForResearchPurposePleaseHaveALookAtTheUserAgentTHXYesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScanningForResearchPurposePleaseHaveALookAtTheUserAgentTHXYesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScanningForResearchPurposePleaseHaveALookAtTheUserAgentTHXYesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScann HTTP/1.1" 404 1389 - Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.86 Safari/537.36
[18/May/2017:18:19:51 +0000] "GET /clientaccesspolicy.xml HTTP/1.1" 404 322 - Cloud mapping experiment. Contact [email protected]

But other traffic is much more malicious. For example, here is what a web server sees while being scanned by the hacking tool ZmEu, which scans web servers trying to find PHPMyAdmin installations to exploit:

[18/May/2017:16:27:39 +0000] "GET /mysqladmin/scripts/setup.php HTTP/1.1" 404 391 - ZmEu
[18/May/2017:16:27:39 +0000] "GET /web/phpMyAdmin/scripts/setup.php HTTP/1.1" 404 394 - ZmEu
[18/May/2017:16:27:39 +0000] "GET /xampp/phpmyadmin/scripts/setup.php HTTP/1.1" 404 396 - ZmEu
[18/May/2017:16:27:40 +0000] "GET /apache-default/phpmyadmin/scripts/setup.php HTTP/1.1" 404 405 - ZmEu
[18/May/2017:16:27:40 +0000] "GET /phpMyAdmin-2.10.0.0/scripts/setup.php HTTP/1.1" 404 397 - ZmEu
[18/May/2017:16:27:40 +0000] "GET /mysql/scripts/setup.php HTTP/1.1" 404 386 - ZmEu
[18/May/2017:16:27:41 +0000] "GET /admin/scripts/setup.php HTTP/1.1" 404 386 - ZmEu
[18/May/2017:16:27:41 +0000] "GET /forum/phpmyadmin/scripts/setup.php HTTP/1.1" 404 396 - ZmEu
[18/May/2017:16:27:41 +0000] "GET /typo3/phpmyadmin/scripts/setup.php HTTP/1.1" 404 396 - ZmEu
[18/May/2017:16:27:42 +0000] "GET /phpMyAdmin-2.10.0.1/scripts/setup.php HTTP/1.1" 404 399 - ZmEu
[18/May/2017:16:27:44 +0000] "GET /administrator/components/com_joommyadmin/phpmyadmin/scripts/setup.php HTTP/1.1" 404 418 - ZmEu
[18/May/2017:18:34:45 +0000] "GET /phpmyadmin/scripts/setup.php HTTP/1.1" 404 390 - ZmEu
[18/May/2017:16:27:45 +0000] "GET /w00tw00t.at.blackhats.romanian.anti-sec:) HTTP/1.1" 404 401 - ZmEu

In addition, servers can also end up receiving unwanted web traffic that is intended for another server. In a cloud environment, an application may end up reusing an IP address that was formerly connected to another service. It’s common for misconfigured or misbehaving DNS servers to send traffic intended for a different host to an IP address now connected to your server.

It’s the responsibility of anyone running a web server to handle and reject potentially malicious traffic or unwanted traffic. Ideally, the web server can reject this traffic as early as possible, before it actually reaches the core application code. A reverse proxy is one way to provide this layer of protection for an application server. It can be configured to reject these requests before they reach the application server.

Reverse proxy for performance

Another advantage of using a reverse proxy such as NGINX is that it can be configured to offload some heavy lifting from your application container. For example, every HTTP server should support gzip. Whenever a client requests gzip encoding, the server compresses the response before sending it back to the client. This compression saves network bandwidth, which also improves speed for clients who now don’t have to wait as long for a response to fully download.

NGINX can be configured to accept a plaintext response from your application container and gzip encode it before sending it down to the client. This allows your application container to focus 100% of its CPU allotment on running business logic, while NGINX handles the encoding with its efficient gzip implementation.

An application may have security concerns that require SSL termination at the instance level instead of at the load balancer. NGINX can also be configured to terminate SSL before proxying the request to a local application container. Again, this also removes some CPU load from the application container, allowing it to focus on running business logic. It also gives you a cleaner way to patch any SSL vulnerabilities or update SSL certificates by updating the NGINX container without needing to change the application container.

NGINX configuration

Configuring NGINX for both traffic filtering and gzip encoding is shown below:

http {
  # NGINX will handle gzip compression of responses from the app server
  gzip on;
  gzip_proxied any;
  gzip_types text/plain application/json;
  gzip_min_length 1000;
 
  server {
    listen 80;
 
    # NGINX will reject anything not matching /api
    location /api {
      # Reject requests with unsupported HTTP method
      if ($request_method !~ ^(GET|POST|HEAD|OPTIONS|PUT|DELETE)$) {
        return 405;
      }
 
      # Only requests matching the whitelist expectations will
      # get sent to the application server
      proxy_pass http://app:3000;
      proxy_http_version 1.1;
      proxy_set_header Upgrade $http_upgrade;
      proxy_set_header Connection 'upgrade';
      proxy_set_header Host $host;
      proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
      proxy_cache_bypass $http_upgrade;
    }
  }
}

The above configuration only accepts traffic that matches the expression /api and has a recognized HTTP method. If the traffic matches, it is forwarded to a local application container accessible at the local hostname app. If the client requested gzip encoding, the plaintext response from that application container is gzip-encoded.

Amazon ECS configuration

Configuring ECS to run this NGINX container as a sidecar is also simple. ECS uses a core primitive called the task definition. Each task definition can include one or more containers, which can be linked to each other:

 {
  "containerDefinitions": [
     {
       "name": "nginx",
       "image": "<NGINX reverse proxy image URL here>",
       "memory": "256",
       "cpu": "256",
       "essential": true,
       "portMappings": [
         {
           "containerPort": "80",
           "protocol": "tcp"
         }
       ],
       "links": [
         "app"
       ]
     },
     {
       "name": "app",
       "image": "<app image URL here>",
       "memory": "256",
       "cpu": "256",
       "essential": true
     }
   ],
   "networkMode": "bridge",
   "family": "application-stack"
}

This task definition causes ECS to start both an NGINX container and an application container on the same instance. Then, the NGINX container is linked to the application container. This allows the NGINX container to send traffic to the application container using the hostname app.

The NGINX container has a port mapping that exposes port 80 on a publically accessible port but the application container does not. This means that the application container is not directly addressable. The only way to send it traffic is to send traffic to the NGINX container, which filters that traffic down. It only forwards to the application container if the traffic passes the whitelisted rules.

Conclusion

Running a sidecar container such as NGINX can bring significant benefits by making it easier to provide protection for application containers. Sidecar containers also improve performance by freeing your application container from various CPU intensive tasks. Amazon ECS makes it easy to run sidecar containers, and automate their deployment across your cluster.

To see the full code for this NGINX sidecar reference, or to try it out yourself, you can check out the open source NGINX reverse proxy reference architecture on GitHub.

– Nathan
 @nathankpeck

Former Vuze Developers Launch BiglyBT, a ‘New’ Open Source Torrent Client

Post Syndicated from Ernesto original https://torrentfreak.com/former-vuze-developers-launch-biglybt-a-new-open-source-torrent-client-170803/

Back in the summer of 2003 a group of developers debuted a new torrent client, which they called Azureus.

BitTorrent itself was still a relatively new technology at the time and users were eager to find new tools to transfer their files. The feature-rich Azureus client, which later rebranded to Vuze, delivered just that.

In recent years, however, things have gone relatively quiet, up to a point where Vuze development appears to have stalled completely. Perhaps not surprising, as two of the core developers, parg and TuxPaper, have left the project and moved on to something new.

“We are no longer involved in Vuze or Azureus Software, Inc. We can not speak to what their intentions are with the development of their product,” they inform us.

The developers, who were also part of the original Azureus team, are not saying farewell to their code though. While they are no longer working on Vuze, the pair have started a new Azureus branch, one they will actively maintain.

“We have invested such a large amount of our lives in the endeavor that we feel the need to keep the open source project active, for both our and our users’ enjoyment!” parg and TuxPaper tell us.

BiglyBT, as they have named their new client, will continue where Vuze development stalled. In addition to optimizing the code and releasing new features, BiglyBT is determined to keep the open source project alive, without any commercial interests.

“Our main goals for BiglyBT is to keep it ad-free and open source, and to continue to develop it into an even better torrent client. We also hope that a community will form again around the product.”

BiglyBT main window (large)

People who try the new client will notice that it’s indeed very similar to Vuze, but without the ads and some other ‘cluttering’ features, such as DVD-burning.

While BiglyBT looks and operates in a similar manner to Vuze, in the future the developers will work on a new set of features, a new style, and various other changes that will set it apart from its older brother.

“Our first release is mostly a name change, but we have removed some of the things that we know users don’t particularly want or use, such as the content network, games promotions, DVD burning, the huge ad in the corner of the app, and the offers in the installer.”

While Vuze appears to have downsized its development efforts, BiglyBT promises to go full steam ahead. The new client will also stay true to the Open Source nature. Previously, some people complained that Vuze included proprietary code, resulting in more restrictive license terms. BiglyBT is purely GPL, and will remain so.

The client is currently available on all major desktop platforms, including Windows, MacOS and Linux. An open source Android app, forked from Vuze remote, will follow in a few weeks.

BiglyBT should appeal to a wide range of users, especially the more seasoned torrent user who wants a client they can configure to their liking.

“Our target users are people who love to delve into the world of torrenting. People who like to tinker and watch torrents do their thing. Hoarders who like to seed, automate, categorize and contribute back to the torrenting community,” the developers note.

People who are interested in giving BiglyBT a spin can download the latest version from the official site. The application is free and won’t install any other applications or adware. Instead, it’s solely supported by donations from the public.

Source: TF, for the latest info on copyright, file-sharing, torrent sites and ANONYMOUS VPN services.

How to Monitor and Visualize Failed SSH Access Attempts to Amazon EC2 Linux Instances

Post Syndicated from Assaf Namer original https://aws.amazon.com/blogs/security/how-to-monitor-and-visualize-failed-ssh-access-attempts-to-amazon-ec2-linux-instances/

As part of the AWS Shared Responsibility Model, you are responsible for monitoring and managing your resources at the operating system and application level. When you monitor your application servers, for example, you can measure, visualize, react to, and improve the security of those servers. You probably already do this on premises or in other environments, and you can adapt your existing processes, tools, and methodologies for use in the AWS Cloud. For more details about best practices for monitoring your AWS resources, see the “Manage Security Monitoring, Alerting, Audit Trail, and Incident Response” section in the AWS Security Best Practices whitepaper.

This blog post focuses on how to log and create alarms on invalid Secure Shell (SSH) access attempts. Implementing live monitoring and session recording facilitates the identification of unauthorized activity and can help confirm that remote users access only those systems they are authorized to use. With SSH log information in hand (such as invalid access type, bad private keys, and remote IP addresses), you can take proactive actions to protect your servers. For example, you can use an AWS Lambda function to adjust your server’s security rules when an alarm is triggered that indicates an invalid SSH access attempt.

In this post, I demonstrate how to use Amazon CloudWatch Logs to monitor SSH access to your application servers (Amazon EC2 Linux instances) so that you can monitor rejected SSH connection requests and take action. I also show how to configure CloudWatch Logs to send SSH access logs from application servers that reside in a public subnet. Last, I demonstrate how to visualize how many attempts are made to SSH into your application servers with bad private keys and invalid user names. Using these techniques and tools can help you improve the security of your application servers.

AWS services and terminology I use in this post

In this post, I use the following AWS services and terminology:

  • Amazon CloudWatch – A monitoring service for the resources and applications you run on the AWS Cloud. You can use CloudWatch to collect and track metrics, collect and monitor log files, set alarms, and automatically react to changes in your AWS resources.
  • CloudWatch namespaces – Containers for metrics. Metrics in different namespaces are isolated from each other so that metrics from different applications are not mistakenly aggregated into the same statistics. You also can create custom metrics for which you must specify namespaces as containers.
  • CloudWatch Logs – A feature of CloudWatch that allows you to monitor, store, and access your log files from EC2 instances, AWS CloudTrail, and other sources. Additionally, you can use CloudWatch Logs to monitor applications and systems by using log data and create alarms. For example, you can choose to search for a phrase in logs and then create an alarm if the phrase you are looking for is found in the log more than 5 times in the last 10 minutes. You can then take action on these alarms, if necessary.
  • Log stream – A log stream represents the sequence of events coming from an application instance or resource that you are monitoring. In this post, I use the EC2 instance ID as the log stream identifier so that I can easily map log entries to the instances that produced the log entries
  • Log group – In CloudWatch Logs, a group of log streams that share the same retention time, monitoring, and access control settings. Each log stream must belong to one log
  • Metric – A specific term or value that you can monitor and extract from log events.
  • Metric filter – A metric filter describes how Amazon CloudWatch Logs extracts information from logs and transforms it into CloudWatch metrics. It defines the terms and patterns to look for in log data as the data is sent to CloudWatch Logs. Metric filters are assigned to log groups, and all metric filters assigned to a given log group are applied to their log stream—see the following diagram for more details.
  • SSH logs – Reside on EC2 instances and capture all SSH activities. The logs include successful attempts as well as unsuccessful attempts. Debian Linux SSH logs reside in /var/log/auth.log, and stock CentOS SSH logs are written to /var/log/secure. This blog post uses an Amazon Linux AMI, which also logs SSH sessions to /var/log/secure.
  • AWS Identity and Access Management (IAM) – IAM enables you to securely control access to AWS services and resources for your users. In the solution in this post, you create an IAM policy and configure an EC2 instance that assumes a role. The IAM policy allows the EC2 instance to create log events and save them in an Amazon S3 bucket (in other words, CloudWatch Logs log files are saved in the S3 bucket).
  • CloudWatch dashboards – Amazon CloudWatch dashboards are customizable home pages in the CloudWatch console that you can use to monitor your resources in a single view, even those resources that are spread across different regions. You can use CloudWatch dashboards to create customized views of the metrics and alarms for your AWS resources.

Architectural overview

The following diagram depicts the services and flow of information between the different AWS services used in this post’s solution.

Here is how the process works, as illustrated and numbered in the preceding diagram:

  1. A CloudWatch Logs agent runs on each EC2 instance. The agents are configured to send SSH logs from the EC2 instance to a log stream identified by an instance ID.
  2. Log streams are aggregated into a log group. As a result, one log group contains all the logs you want to analyze from one or more instances.
  3. You apply metric filters to a log group in order to search for specific keywords. When the metric filter finds specific keywords, the filter counts the occurrences of the keywords in a time-based sliding window. If the occurrence of a keyword exceeds the CloudWatch alarm threshold, an alarm is triggered.
  4. An IAM policy defines a role that gives the EC2 servers permission to create logs in a log group and send log events (new log entries) from EC2 to log groups. This role is then assumed by the application servers.
  5. CloudWatch alarms notify users when a specified threshold has been crossed. For example, you can set an alarm to trigger when more than 2 failed SSH connections happen in a 5-minute period.
  6. The CloudWatch dashboard is used to visualize data and alarms from the monitoring process.

Deploy and test the solution

1.  Deploy the solution by using CloudFormation

Now that I have explained how the solution works, I will show how to use AWS CloudFormation to create a stack with the desired solution configuration. CloudFormation allows you to create a stack of resources in your AWS account.

  1. Sign in to the AWS Management Console, choose CloudFormation, choose Create Stack, choose Specify an Amazon S3 template URL and paste the following link in the box: https://s3.amazonaws.com/awsiammedia/public/sample/MonitorSSHActivities/CloudWatchLogs_ssh.yaml
    Screenshot of creating the stack
  2. Choose Launch to deploy the stack.
  3. On the Specify Details page, enter the Stack name. Then enter the KeyName, which is the SSH key pair for the region you use. I use this key-pair later in this post; if you don’t have a key pair for your current region, follow these instructions to create one. The OperatorEmail is the CloudWatch alarm recipient email address (this field is mandatory to launch the stack), which is the email address to which SSH activity alarms will be sent. You can use the SSHLocation box to limit the IP address range that can access your instances; the default is 0.0.0/0, which means that any IP can access the instance. After specifying these variables, click Next.
    Screenshot of the "Specify Details" page
  4. On the Options page, tag your instance, and click Next. Tags allow you to assign metadata to AWS resources. For example, you can tag a project’s resources and then use the tag to manage, search for, and filter resources. For more information about tagging, see Tagging Your Amazon EC2 Resources.
    Screenshot of the "Options" page
  5. Wait until the CloudFormation template shows CREATE_COMPLETE, as shown in the following screenshot. This means your stack was created successfully.
    Screenshot showing the stack was created successfully

After the stack is created successfully, you have two distinct application servers running, each with a CloudWatch agent. These servers represent a fleet of servers in your infrastructure. Choose the Outputs tab to see more details about the resources, such as the public IP addresses of the servers. You will need to use these IP addresses later in this post in order to trigger log events and alarms.

Screenshot showing the public IP addresses of the servers

The CloudWatch log agent on each server is installed at startup and configured to stream SSH log entries from /var/log/secure to CloudWatch via a log stream. CloudWatch aggregates the log streams (ssh.log) from the application servers and saves them in a CloudWatch Logs log group. Each log stream is identified by an instance-ID, as shown in the following screenshot.

Screenshot of two log streams' instance-IDs

The application servers assume a role that gives them permissions to create CloudWatch Logs log files and events. CloudFormation also configures two metrics: ssh/InvalidUser and ssh/Disconnect. The ssh/InvalidUser metric sends an alarm when there are more than 2 SSH attempts into any server that include an invalid user name. Similarly, the ssh/Disconnect metric creates an alarm when more than 10 SSH disconnect requests come from users within 5 minutes.

To review the metrics created by CloudFormation, choose Metrics in the CloudWatch console. A new SSH custom namespace has been created, which contains the two metrics described in the previous paragraph.

You should now have two application servers running and two custom CloudWatch metrics and alarms configured. Now, it’s time to generate log events, trigger alarms, and test the configurations.

2.  Test SSH metrics and alarms

Now, let’s try to trigger an alarm by trying to SSH with an invalid user name into one of the servers. Use the key pair you specified when launching the stack and connect to one of the Linux instances from a terminal window (replace the placeholder values in the following command).

ssh -i <the-key-pair-you-specified-in-the-CloudFormation-template> ec2-user@<ec2 DNS or IP address>

Now, exit the session and try to sign in as bad-user, as shown in the following command.

ssh -i <the-key-pair-you-specified-in-the-CloudFormation-template> bad-user@<ec2 DNS or IP address>

The following command is the same as the previous command, but with the placeholder values replaced by actual values.

ssh -i "my-keycap" bad-user@ec2-XX-XXX-XXX.compute-1.amazonaws.com

Because the alarm triggers after two or more unsuccessful SSH login attempts with an invalid user name in 1 minute, run the preceding command a few times. The server’s log captures the bad SSH login attempts, and after a minute, you should see InvalidUserAlarm in the CloudWatch console, as shown in the following screenshot. Choose Alarms to see more details. The alarm should disappear after another minute if there are no more SSH login attempts.

Screenshot showing InvalidUserAlarm in the CloudWatch console

You can also view the history of your alarms by choosing the History tab. CloudWatch metrics are saved for 15 months.

When the CloudFormation stack launches, a topic-registration email is sent to the email address you specified in the template. After you accept the topic registration, you will receive an alarm email with details about the alarm. The email looks like what is shown in the following screenshot.

Screenshot of the alarm email

3.  Understanding CloudWatch metric filters and their transformation

The CloudFormation template includes two alarms, InvalidUserAlarm and SSHReceiveddisconnectAlarm, and two metric filters. As I mentioned previously, the metric filters define the pattern you want to match in a CloudWatch Logs log group. When a pattern is found, it transforms into an Amazon metric as defined in the MetricTransformations section of the metric filter.

The following is a snippet of the InvalidUser metric filter. Each pattern match—denoted by FilterPattern—is counted as one metric value as specified in the MetricValue parameter in the MetricTranformations section. The CloudWatch alarm associated with this metric filter will be triggered when the metric value crosses a specified threshold.

  InvalidUser:
    Type: AWS::Logs::MetricFilter
    Properties:
      LogGroupName:
        Ref: WebServerLogGroup
      FilterPattern: "[Mon, day, timestamp, ip, id, status = Invalid, ...]"
      MetricTransformations:
      - MetricValue: '1'
        MetricNamespace: SSH
        MetricName: sshInvalidUser

When a CloudWatch alarm is triggered, the service sends an email to an Amazon SNS topic with more details about the alarm type, trigger parameters, and status.

4.  Create a CloudWatch metric filter to identify attempts to SSH into your servers with bad private keys

You can create additional metric filters in CloudWatch Logs to provide better visibility into the SSH activity on your servers. Let’s assume you want to know if there are too many attempts to SSH into your servers with bad private keys. If an attempt is made with a bad private key, a line like the following is logged in the SSH log file.

Apr 27 21:44:49 ip-172-31-38-5 sash[28515]: Connection closed by <ip address> [preauth]

You can produce this log line by modifying the pem file you are using (a pem file holds your private key). In a terminal window, modify your private key by copying and pasting the following lines in the same directory where your key resides.

$ cat <valid-pem-file> | sed s/./A/25 > bad-keys.pem

$ cat bad-keys.pem | sed s/./A/26 > bad-keys.pem

These lines simply change the characters at positions 25 and 26 from their current value to the character A, keeping the original pem file intact. Alternatively, you can use nano <valid-keys>.pem from the command line or any other editor, change a character, save the file as bad-keys.pem, and exit the file.

Now, try to use bad-keys.pem to access one of the application servers.

ssh  -i "bad-keys.pem" [email protected]

The SSH attempt should fail because you are using a bad private key.

Permission denied (public key).

Now, let’s look at the server’s ssh.log file from the CloudWatch Logs console and analyze the error log messages. I need to understand the log format in order to configure a new filter. To review the logs, choose Logs in the navigation pane, and select the log group that was created by CloudFormation (it starts with the name you specified when launching the CloudFormation template).

Screenshot of the ssh.log file

In particular, notice the following line when you try to SSH with a bad private key.

Screenshot of one line of the ssh.log file

Let’s add a metric filter to capture this line so that we can use this metric later when we build an SSH Dashboard. Copy the following line to the Filter events search box at the top of the console screen and press Enter.

[Mon, day, timestamp, ip, id, msg1= Connection, msg2 = closed, ...]

Screenshot of pasting the line from the ssh.log file into the "Filter events" search box

You can now see only the lines that match the pattern you specified. These are the lines you want to count and transform into metrics. Each string in the message is represented by a word in the filter. In our example, we are looking for a pattern where the sixth word is Connection and the seventh word is closed. Other words in the log line are not important in this context. The following image depicts the mapping between a string in a log file and a metric filter.

To create the metric filter, choose Logs in the navigation pane of the CloudWatch console. Choose the log groups to which you want to apply the new metric filter and then choose Create Metric Filter. Choose Next.

Screenshot of creating a metric filter

Paste the filter pattern we used previously (the sixth word equals Connection and the seventh word equals closed) in the Filter Pattern box. Select the server you tried to sign in to with the bad private key to Select Log Data to Test and click Test Pattern. You should see the results that are shown in the following screenshot. When completed, click Assign Metric.

Screenshot of applying the filter pattern

Type SSH for the Metric Namespace and sshClosedConnection-InvalidKeysFilter for Filter Name. Choose Create Filter to see your new metric filter listed. You can use the newly created metric filter to graph the metrics and set alarms. The alarms can be used to inform your administrator via email of any event you specify. In addition, metrics can be used to generate SNS notification to trigger an AWS Lambda function in order to take proactive actions, such as blocking suspicious IP addresses in a security group.

Screenshot of creating a filter

Choose Create Alarm next to Filter Name and follow the instructions to create a CloudWatch alarm.

Screenshot of the "Create Alarm" option

Back at the Metrics view, you should now have three SSH metric filters under Custom Namespaces. Note that it can take a few minutes for the number of SSH metrics to update from two to three.

Screenshot showing you now have three SSH metric filters

5.  Create a graph by using a CloudWatch dashboard

After you have configured the metrics, you can display SSH metrics in a graph. CloudWatch dashboards allow you to create reusable graphs of AWS resources and custom metrics so that you can quickly monitor your operational status and identify issues at a glance. Metrics data is kept for a period of two weeks.

In the CloudWatch console, choose Dashboards in the navigation pane, and then choose Create dashboard to create a new graph in a dashboard. Name your dashboard SSH-Dashboard and choose Create dashboard. Choose Line Graph from the graph options and choose Configure.

Screenshot of creating a CloudWatch dashboard

In the Add metric graph window under Custom Namespace, choose SSH > Metrics with no dimensions. Select all three metrics you have configured (the CloudFormation template configured two metrics and you manually added one more metric).

By default, the metrics are displayed on the graph as an average. However, you configured metrics that are based on summary metrics (for example, the total number of alarms in two minutes). To change the default, choose the Graphed metrics tab, and change the statistic from Average to Sum, as shown in the following screenshot. Also, change the time period from 5 minutes to 1 minute.

Screenshot of changing the statistic from "Average" to "Sum"

Your graphed metrics should look like the following screenshot. When you have provided all the necessary information, choose Create Widget.

Screenshot of thet graphed metrics

You can rename the graph and add static text to give the console more context. To add a text widget, choose Widget and select text. Then edit the widget with markdown language. Your dashboard may then look like the following screenshot.

The consolidated metrics graph displays the number of SSH attempts with bad private keys, invalid user names, and too many disconnects.

Conclusion

In this blog post, I demonstrated how to automate the deployment of the CloudWatch Logs agent, create filters and alarms, and write, test, and apply metrics on the fly from the AWS Management Console. You can then visualize the metrics with the AWS Management Console. The solution described in this post gives you monitoring and alarming capabilities that can help you understand the status of and potential risks to your instances and applications. You can easily aggregate logs from many servers and different applications, create alarms, and display logs’ metrics on a graph.

If you have comments about this post, submit them in the “Comments” section below. If you have questions about the solution in this post, start a new thread on the CloudWatch forum.

– Assaf

A new twist on data backup: CloudNAS

Post Syndicated from Andy Klein original https://www.backblaze.com/blog/cloudnas-backup/

Morro CacheDrive

There are many ways for SMBs, professionals, and advanced users to back up their data. The process can be as simple as copying files to a flash drive or an external drive, or as sophisticated as using a Synology or QNAP NAS device as your primary storage device and syncing the files to a cloud storage service such as Backblaze B2.

A recent entry into the backup arena is Morro Data and their CloudNAS solution, where files are stored in the cloud, cached locally as needed, and synced globally among the other CloudNAS systems in a given organization. There are three components to the solution:

  • A Morro CacheDrive — This resides on your internal network like a NAS device and stores from 1- to 8 TB of data depending on the model
  • The CloudNAS service — This software runs on the Morro CacheDrive to keep track of and manage the data
  • Backblaze B2 Cloud Storage — Where the data is stored in the cloud

The Morro CacheDrive is installed on your local network and looks like a network share. On Windows, the share can be mounted as a letter device, M:, for example. On the Mac, the device is mounted as a Shared device (Databank in the example below).

CloudNAS software dashboard

In either case, the device works like a folder/directory, typically on your desktop. You then either drag-and-drop or save a file to the folder/directory. This places the file on the CacheDrive. Once there, the file is automatically backed up to the cloud. In the case of CloudNAS solution, that cloud is Backblaze B2.

All that sounds pretty straight-forward, but what makes the CloudNAS solution unique is the solution allows you to have unlimited storage space. For example, you can access 5 TB of data from a 1 TB CacheDrive. Confused? Let me explain. All 5 TB of the data is stored in B2, having been uploaded to B2 each time you stored data on the CacheDrive. The 1 TB CacheDrive keeps (caches) the most recent or most often used files on the CacheDrive. When you need a file not currently stored on the CacheDrive, the CloudNAS software automatically downloads the file from the B2 cloud to the CacheDrive and makes it available to use as desired.

Things to know about the CloudNAS solution

  • Sharing Systems: Multiple users can mount the same CacheDrive with each being able to update and share the files.
  • Synced Systems: If you have two or more CloudNAS systems on your network, they will keep the B2 directory of files synced between all of the systems. Everyone on the network sees the same file list.
  • Unlimited Data: Regardless of the size of the CacheDrive device you purchase, you will not run out of space as Backblaze B2 will contain all of your data. That said, you should choose the size of your CacheDrive that fits your operational environment.
  • Network Speed: Files are initially stored on the CacheDrive, then copied to B2. Local network connections are typically much faster than internet network speeds. This means your files are uploaded to the CacheDrive fast then transferred to B2 as time allows at the speed of your internet connection, all without slowing you down. This should be interesting to those of you who have slower internet connections.
  • Access: The files stored using the Cloud NAS solution can be accessed through the shared folder/directory on your desktop as well as through a web-based Team Portal.

Getting Started

To start, you purchase a Morro CacheDrive. The price starts at $499.00 for a unit with 1 TB of cache storage. Next you choose a CloudNAS subscription. This starts at $10/month for the Standard plan, and lets you manage up to 10 TB of data. Finally, you connect Backblaze B2 to the Morro system to finish the set-up process. You pay Backblaze each month for the data you store in and download from B2 while using the Morro solution.

The CloudNAS solution is certainly a different approach to storing your data. You get the ability to store a nearly unlimited amount of data without having to upgrade your hardware as you go, and all of your data is readily available with just a few clicks. For users who need to store terabytes of data that needs be available anytime, the CloudNAS solution is worth a look.

The post A new twist on data backup: CloudNAS appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.