TKP Courseware Influences

We often get asked ‘what are the influences’ for TKP courseware? TKP courseware includes TKPJava, TKPSmallBasic and new courseware around Data Science and IoT concepts.

In addition to to the work of the TKP team that has created TKPJava courseware, the team is inspired by many other influences.  These influences are varied and many (and listed below), in particular the ideas in this book inspire many of our lesson concepts:

TurtleGeometry

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Trying out CloudBerry Lab Explorer Pro with Google Cloud Storage and Nearline

Tooling matters – particularly in the new-to-many-customers cloud world.  To that end, I’ve been using cloud storage management tools from CloudBerry Lab with several Enterprise customers and made a quick screencast demo of their Storage Explorer Pro (in this case for GCP – Google Cloud Storage and Nearline).

In addition to GCP, CloudBerry Lab makes cloud storage products which work with AWS, Azure, and more.

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Whitepaper – Streaming Hadoop Solutions

In this whitepaper, I take a look at the various options for Hadoop Streaming.  These include Apache Storm, Apache Spark Streaming and Apache Samza.  Also I examine commercial alternatives, such as Data Torrent.  I cover implementation details of streaming, including type of streaming and capacities of libraries and products included.

You can read this whitepaper online or download it via the included Slideshare link.

Happy streaming!

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Whitepaper – Practical Machine Learning

Here’s a whitepaper I wrote on the ‘state of Machine Learning’.  It includes information about implementation via various cloud-based ML services (AWS, Azure, IBM) as well as category information (for architects).  Your are welcome to read this whitepaper online or to download it if you prefer (linked to Slideshare source).

Enjoy!

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Using Premium Data – for Business Analysts

Here’s the deck for my talk at SQLSaturday and also at SQLPASS BA conference.

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Spring and Summer Speaking Schedule for Lynn Langit

Spring and Summer 2015 speaking schedule

Gigaom Structure Data

In March

  • Lynda.com – recording a new course – here’s a link to my current courses (‘Hadoop Fundamentals’ and ‘Learning Visual Programming with Kodu’)
  • GigaOm Structure Data conference in NYC (March 17-19) – link here, speaking on the SaaS BI Solution Panel
  • TKP in NYC (March 19-20) – for private teacher training event, teaching teachers to teach kids Java

In April 

  • SQL Saturday 389 (April 11) in Huntington Beach, CA – link here
  • SQL PASS Business Analytics (April 20-22) in Santa Clara, CA – link here – speaking on ‘Premium Data for Analysts’ (Dun & Bradstreet is currently surveying potential customers – take survey here ) and also presenting a preCon ‘3 Tools per Hour – 24 FREE Tools for Analysts’

In May

  • DevIntersection Conference (May 19-21) in Scottsdale, AZ – link here– speaking on ‘The Azure Data Story’ and ‘AWS vs Azure – for the Architect’ (w/Michele Bustamante)

In June

  • Norwegian Developer’s Conference (June 15-19) in Oslo, Norway – link here–  speaking on ‘AWS vs. Azure for the Architect’ (w/ Michele Bustamante) and on other cloud topic TBD.

In July

  • Launching new TKP courseware (all month) in San Diego, CA – training teachers and teachers on core #TKPJava and #IoTDataScience
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Lessons Learned – Benchmarking NoSQL on the AWS Cloud (AerospikeDB and Redis)

AWS EC2

AWS EC2

In this post I’ll summarize what I learned from running benchmark tests on virtual machines on the AWS Cloud with the Aerospike team and also as I validated their test results independently. I’ll also discuss benchmarking techniques & results for this particular set of test databases. In the process of validating benchmarks, I learned many broadly applicable AWS-specific EC2 benchmarking practices that I will include.

I tested two NoSQL databases – Aerospike and Redis. Both databases are known for speed and are often used for caching or as fast key value stores via in-memory implementation. Aerospike is built to be extremely fast by leveraging SSDs for persistence and to be very easy to scale. By contrast, Redis is built primarily as a fast in memory store.

Aerospike is multithreaded and Redis is single threaded. For the benchmark tests, I compared both as simple key-value stores. To fairly compare, I needed to scale out Redis so that it uses multiple cores on each AWS EC2 instance. The way to do this is to launch several Redis servers and shard the data among these servers.

Benchmark Results — TL; DR – at scale Aerospike wins

As I compared both databases at scale, I found a key differentiator to be manageability of sharding or scaling for each type of database solution.

Redis

Redis

About Redis Scaling:

  • You must manage sharding yourself, by coming up with a sharding algorithm that evenly balances data between the shards.
  • Some of the Java clients (such as Jedis) do this for you, but you must check to make sure that the data is properly balanced.
  • If you wish to increase the number of Redis shards in order to increase throughput or data volume, you will have to refactor the sharding algorithm and rebalance the data. This usually results in a downtime.
  • If you need replication and failover, you will need to synchronize the data yourself, at the application layer.
Aerospike

Aerospike

About Aerospike Scaling:

  • Aerospike handles the equivalent of sharding automatically.
  • There is no downtime. You can add new capacity (data volume and throughput) dynamically. Simply add additional nodes and Aerospike will automatically rebalance the data and traffic.
  • You set the replication factor to 2 or more and configure Aerospike to operate with synchronous replication for immediate consistency.
  • If a server goes down, Aerospike handles fail-over transparently; the Aerospike client makes sure that your application can read or write replicated data automatically.
  • You can run purely in RAM, or take advantage of Flash/SSDs for storage. Using Flash/SSDs results in a slight penalty on latency (~0.1ms longer on average), but allows you to scale with significantly smaller clusters and at a fraction of the cost.

Benchmark Testing on AWS — TL; DR  – the devil is in the details

Although AWS is convenient and inexpensive to use for testing, cloud platforms like AWS typically, demonstrate greater variability of results. The network throughput, disk speeds, etc are more variable and this may result in different throughput results for the tests when conducted in a different availability zone, at a different time of day or even within the same run of the test. Using AWS boundary containers, such as an AWS VPC and an AWS Placement Group reduces this variability by a significant amount.

That being said, I found that reproducing vendor benchmarks on any public cloud requires quite a bit of attention to detail. The environment is obviously different that on premises. Also beyond basic set up, performance-tuning techniques vary from those I’ve used for on premise and also from cloud-to-cloud solutions. In addition to covering the steps to do this, I’ve also included a long list of technical links at the end of this blog post.

Part 1: Getting Setup to Test on AWS – the Basics

Step 1 –  Create an IAM AWS User account. I performed all of my tests as an authorized AWS IAM (non root) user. It is of course a best practice for all use of any cloud to run as least privileged user, rather than root. On AWS via IAM there are permission templates, which make the creation of users and assignment of permission quick and easy, and there is really no excuse to perform benchmark testing as a root user.

Step 2 – Select your EC2 AMI. For the first, most basic type of test, you’ll need to select, start and configure 3 AWS EC2 instances. There are a number of considerations here. In this post, the term “node” means a single EC2 instance and “shard” will mean a single Redis process acting as a part of a larger database service.

To get started, I used three of the same Amazon Linux AMIs. Each instance should be capable of having HVM enabled for maximum network throughput. HVM provides enhanced networking, it uses single root I/O virtualization (SR-IOV) and results in higher network performance (packets per second), lower latency and lower jitter.

I used Amazon Linux AMI version 2014.09, as shown below:

AWS EC2 Linux instance

AWS EC2 Linux instance

Step 3 – Select your AWS EC2 Machine Types. I chose the AWS R3 series of instances, since these were designed to be optimized for memory intensive applications. Specifically I used R3.8xlarge which has 32 CPUs and 244 GB RAM for the servers. On this instance type, HVM should be enabled by default so long as you spin up your instances in an AWS VPC.

AWS Component Type CPUs RAM SSD ENIs Network Use
EC2 instance R3.8xlarge 32 244 2 x 320 4 10 Gigabit Redis server
EC2 instance R3.8xlarge 32 244 2 x 320 4 10 Gigabit Aerospike server
EC2 instance R3.2xlarge 8   61 1 x 160 2 “High” Database client

Step 4 – Create an AWS Placement Group. As you prepare to spin up each EC2 instance be sure to use AWS containers to simulate the ‘in the same rack’ proximity that you’d have if you were performing tests on premise. In order to exactly simulate this and to minimize network latency on the AWS Cloud, I was careful to place the first set of EC2 instances in the same VPC, availability zone and placement group. About AWS Placement groups from AWS documentation “A placement group is a logical grouping of instances within a single Availability Zone. Using placement groups enables applications to participate in a low-latency, 10 Gbps network.”

Step 5 –  Startup your 3 EC2 instances. Be sure to place them in the same VPC, availability zone and placement group. Take note of both their external and internal IP addresses.

Step 6 –  Connect to each of your instances. When you connect you may also want to verify HVM for each one, to do so run this command to verify that the ixgbevf driver has been properly installed as shown below:

Ethtool

Ethtool

Step 7 – Add more AWS ENIs: Even with Enhanced Networking, the network throughput is not enough to drive Aerospike and Redis to their capacity. To increase the network throughput I added more network interfaces or ENIs to each server. By using 4 ENIs on each r3.8xlarge EC2 instance I reached high network throughputs where the database engines load the CPU cores to a significant amount (around 40%-60%).

Although you will add these ENIs (and also associate them with the EC2 instances for the servers and client, you will also need to perform additional configuration steps to get maximum throughput. These steps are described in the ‘AWS Performance Networking Tuning’ section of this post.

Also when connecting from the client to the server for testing, I used the internal IP address to utilize the containment that I had so carefully set up. Shown below is a simple diagram of this process.

AWS Architecture

AWS Architecture 

Part 2: Installing the Databases and Testing the Benchmark Tools

This first ‘test’ is purposefully simple and isn’t really designed to test either database at capacity, rather it’s a kind of “Hello World” or “smoke test” designed to test your testing environment. Benchmark 1 tests with a single node for each database server and keep all data in memory only, i.e. no data is persisted to disk. To proceed you perform the following steps:

  • Step 1 – Install Redis (2.8.17) on one EC2 R3.8xlarge instance
  • Step 2 – Install Aerospike (3.3.21 Community) on another EC2 R3.8xlarge instance
  • Step 3 – Install Client Tools – Aerospike Java client (3.0.30) and Redis Jedis client (2.6.0) on a EC2 R3.2xlarge instance
  • Step 4 – Install the Benchmark Tools on the client – I used 3 tools – the Aerospike and Redis-version (of Aerospike) benchmarking tool and also the native Redis benchmarking tool. Benchmark test results for Redis were roughly the same using either the Aerospike benchmark tool for Redis or the native Redis tool.
  • Step 5 – Test run the benchmarks – at this point you are only trying to verify that you’ve installed the server(s), client and benchmarking tools correctly. You will not get highest level benchmark results until AFTER you perform the additional AWS performance tweaks, listed in the next section. You can see the type of activity (read or write) in the first column, then going across, and the latency for operations in ms by percentage of operations. I’ve highlighted the total transactions per second in the red circle in the first sample output below.

Shown below is sample output from the Aerospike benchmark tool:

Aerospike Performance Tool

Aerospike Performance Tool 

Shown below is sample output from the native Redis benchmark tool:

Redis Performance Tool

Redis Performance Tool

Part 3a: Run Tests -> Benchmark Test 1 – Single node, no persistence

For this first benchmark, I tested the performance of both Aerospike and Redis as a completely RAM-based store. To get a more realistic result that just running the benchmark in a ‘plain vanilla’ configuration, you will want to compensate for architectural differences in the products. Aerospike is multithreaded and will use all available cores (which in our case is 32 per server instance), while Redis is single-threaded. To fairly compare, I launched multiple instances of Redis and sharded the data manually. Shown below is a visualization of this process.

The first diagram shows this process for Aerospike:

Aerospike Architecture

Aerospike Architecture

All clients must run with all the shards configured. Otherwise, the partitioning of keys will break down. Because of this, all the benchmark clients should send traffic to all the redis servers.

The diagram below shows this process for Redis:

Redis Architecture

Redis Architecture

Here is the process to add Redis shards:

  • Run redis-*/utils/install-server.sh and use sequentially increasing port numbers (up from 6379)
  • Change conf file for every server (/etc/redis/6380.conf) and comment out the “save” lines to disable persistence
    for i in {6387..6394}; do sed -i ‘s/^save/#save/’ $i.conf; done
  • Restart the redis instances
  • Make sure you “FLUSHALL” in redis-cli for all redis instances
    for i in {6379..6386}; do redis-cli -p $i FLUSHALL; done
  • After keys have been inserted, confirm that they are equally distributed among the shards using “INFO” command in redis-cli. The last line in the output shows the number of records.
    for i in {6379..6386}; do redis-cli -p $i INFO | grep “keys=”; done

The next set of considerations is around mitigating the network bottleneck that you will encounter when testing these high performance databases with the default number of ENIs (network interfaces). Here is where you will want to further ‘tune’ those additional ENIs that we created when we set up the instances by configuring IRQ and Process affinity manually. The next section details this process.

AWS Networking Performance Tuning

  • IRQ affinity: Both Aerospike and Redis are extremely fast in their in-process operations of getting and setting data items. To this end, it is beneficial to dedicate CPU cores to handle just the network IRQs. Each network interface has 2 IRQs, which can be found by:

    for i in {0..3}; do echo eth$i; grep eth$i-TxRx /proc/interrupts | awk ‘{printf ” %s\n”, $1}’; done

    Now, we can assign one CPU core for processing interrupts on each IRQ by changing the smp_affinity values of the IRQs.

    echo 1 > /proc/irq/259/smp_affinity
    echo 2 > /proc/irq/260/smp_affinity

  • Process affinity: The kernel on any of the CPU cores may schedule the Aerospike and Redis processes and indeed they may switch around different cores in the course of a single run. It has been empirically found that pinning the processes to a set of CPU cores usually results in better performance. Specifically, keeping the CPU cores handling the network IRQ isolated from the database processes is beneficial. This is accomplished by using the taskset command. To make Aerospike use CPU cores 8 to 31:

    # taskset -ap FFFFFF00 <PID of asd>

    In the case of the many sharded Redis processes, this takes many taskset invocations – one for each Redis server. As Redis is single threaded, assigning one Redis server for each CPU core is a good idea.

To configure your environment you perform the following steps:

  • Step 1 – Install multiple instances of Redis on your Redis server instance
  • Step 2 – Verify that you have the correct number of ENIs associated to your server(s)
  • Step 3 – Spin up additional client instances – for this test, I used 4 instances
  • Step 4 – Install Client Tools and Benchmark Tool on each client instance
  • Step 5 – Assign CPUs to IRQs as per the directions in the previous section (smp_affinity)
  • Step 6 – Pin processes to CPUs as per the directions in the previous section (taskset)
  • Step 7 – Set the default RAM for the “test” Aerospike namespace to 10 GB
  • Step 8 – Set each Redis instance to disable snapshotting by commenting out the “save” parameters in the config file.
  • Step 9 – Run the benchmark tool (using parameters) and compare the tuned benchmarks

Benchmark Tool Parameters

The multiple hosts in the “-h” option of the benchmark tool must be used to test against sharded Redis servers. The ports are assumed to be serially increasing from the number specified in the “-p” option. Benchmark options used in the current tests were:

  • 10 million keys (-k 10000000)
  • 100 byte string values for each key (-o S: 100)
  • Three different read-write loads:
    • 50%-50% read/write (-w RU, 50)
    • 80%-20% read/write (-w RU, 80)
    • 100% read (-w RU, 100)
  • Before every test run, data was populated using the insert workload option (-w I)
  • For each client instance, 90 threads give the maximum throughput in the in-memory database case (-z 90). This was reduced to a lower number in case of persistence tests (Benchmark Test 2) to avoid write errors due to disk choking.

Benchmark Test 1 – Results

Aerospike is as fast as Redis with close to 1 MTPS for 100% read workloads on a single node on AWS R3.8xlarge with no persistence.

The default bottleneck in both cases is the network throughput of the instances. Adding ENIs helps to increase the TPS for both Aerospike and Redis. With proper network IRQ affinity and process affinity set, both reach close to 1 MTPS in the 100% read workload. The chart below shows the benchmark test 1 results.

Benchmark Test 1 Results

Benchmark Test 1 Results

Part 3b: Run Tests ->Benchmark Test 2 – Single Node, with Persistence

In this scenario, persistent storage was introduced. All of the data was still in memory but was also persisted on EBS SSD (gp2) storage.

For Aerospike a new namespace was configured for this case. The “data-in-memory” config parameter was used. To avoid the bottleneck caused by writing to single file, Aerospike was configured to write to 12 different data file locations (to create the same environment as the 12 files written by the 12 Redis shards.) This configuration specifies that the storage files will only be read when restarting the instance.

The append-only file persistence option (AOF) was used to test with Redis. When a certain size of the AOF file is reached, Redis compacts the file by reading the data from memory (background rewriting AOF). When this was taking place, there are periods when Redis throughput dropped. To avoid these outlier numbers, I kept the auto-aof-rewrite-min-size parameter to a large size so that the rewrites were not triggered while the benchmark was being run. These changes favorably overstate Redis performance.

Benchmark Test 2 Results

Benchmark Test 2 Results

Benchmark Test 2 – Results

As shown in the chart above, Aerospike is slightly faster than Redis for 100/0 and 80/20 read/write workloads against a single node backed by EBS SSD (gp2) storage for persistence.

I ran the test against 12 Redis shards on a single machine with 4 ENIs.. In this scenario, it was the disk writes which were the bottleneck. The number of client threads was reduced for both Aerospike and Redis, to keep write errors to zero.

It is important to note that Aerospike handles rewrites of the data using a block interface, rather than appending to a file. It uses a background job to rewrite the data. The throughput numbers presented above are a good representation of the overall performance. However, when using a persistence file, Redis must occasionally rewrite the data from RAM to disk in an AOF rewrite. During these times peak throughput is reduced. The throughput results above do not take AOF rewrites into account.

The effects of AOF Rewrites should not be underestimated. In the above charts, I configured Redis to not do this, since it is difficult to measure the steady state performance of the database during this time. However, it is important to understand its effects since this may impact your production system. The chart below shows how Redis performs during one example of an AOF rewrite. Notice that both the read and write performance varies during the rewrite.

Redis AOF

Redis AOF

References

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