Monitoring Go programs with Riemann

uSwitch.com uses Riemann to monitor the operations of the various applications and services that compose our system. Riemann helps us aggregate systems and application metrics together and quickly assemble dashboards to understand how things are behaving now.

Most of these services and applications are written in Clojure, Java or Ruby, but recently we needed to monitor a service we implemented with Go.

Hopefully this is interesting for Go authors that haven’t come across Riemann or Clojure programmers who’ve not considered deploying Go before. I think Go is a very interesting option for building systems-type services.

Whence Go?

Recently at uSwitch the team I work on started replacing a small but core service from a combination of Java and Clojure programs to Go.

We’ve done this for probably 3 main reasons:

  1. Deploying a statically-linked binary with few other runtime dependencies (such as a JVM) makes deployment simpler and the process overhead smaller.
  2. Go’s systems roots makes it feel like your programming closer to the OS. Handling socket errors, process signals etc. They all feel better than writing through the Java (or other) abstractions.
  3. It also gives us the chance to experiment with something different in a small, contained way. This is the way I/we have been building systems for the last few years at uSwitch and for 7 or 8 years prior at TrafficBroker and Forward: we help systems emerge and evolve from smaller pieces. It provides a way to safely, and rapidly, learn about the best way to apply tools and languages in different situations through experimentation.

Monitoring systems

Riemann is a wonderful tool for monitoring the performance and operational behaviour of a system.

We’ve gone from using it to just aggregate health checks and alerting to running live in-production experiments to understand the best value EC2 hardware to run the system on. It’s proved both versatile and valuable.

For some of the services we’ve built Riemann is the primary way to see what the application is doing. We no longer build application-specific dashboards or admin interfaces. For example, our open-source systems Blueshift and Bifrost do this.

Getting data into Riemann

Riemann is really just an event stream processor: it receives incoming events and applies functions to produce statistics and alerts.

Although it’s possible for applications to write events directly- Riemann uses Protocol Buffers and TCP/UDP directly so its relatively easy to build clients. We’ve found it far easier, however, to use Metrics- a Java library that provides some slightly higher-level abstractions: Timers, Counters and more making it much cleaner and easier to integrate.

Although the original Metrics library was written for Java, an equivalent has been written in Go: go-metrics. We’ve contributed a Riemann reporter which is currently sitting inside a pull request, until then you can also use my fork with Riemann support.

Go Example reporting metrics to Riemann

Let’s consider an example to help understand how it can be helpful.

We’re building a service which will listen for TCP connections, perform some operation and then close the connection. One of the most obvious useful things we can monitor is the number of open socket connections we have. This might be useful for monitoring potential resource exhaustion, performance degradation or, just to see how many clients you have connected.

Our example service will: accept a connection, say hello, sleep for 5 seconds, close the connection. We’ll use a Counter metric that we’ll increment and decrement when clients open and close connections.

We create connCounter and register it in the metrics.DefaultRegistry before getting on with our work. As our program runs the registry stores the metric values. After registering our metric we start a goroutine that will periodically report all metric values to a Riemann instance. Note that our program imports my fork of go-metrics.

Our handleConnection function is responsible for servicing connected clients. It first increments the connCounter and then defers a decrement operation so we’ll decrement when our function returns.

This is a pretty trivial example, most of our services expose multiple different metrics recording things like counters for the number of open files, timers for the speed we can service requests (which will automatically calculate summary statistics), meters for whenever we experience an error etc.

Running the example

Although Riemann supports many different outputs we frequently use riemann-dash which lets you easily create (and save) custom dashboards with graphs, logs, gauges and more.

To help see what our example service does when its run I’ve recorded a small video that shows us running the service and a riemann-dash plot showing the number of open connections. After we’ve started the service we connect a couple of clients with telnet, the plot moves as connections are opened (and closed).

Introducing Bifrost: Archive Kafka data to Amazon S3

We're happy to announce the public release of a tool we've been using in production for a while now: Bifrost

We use Bifrost to incrementally archive all our Kafka data into Amazon S3; these transaction logs can then be ingested into our streaming data pipeline (we only need to use the archived files occasionally when we radically change our computation).

Rationale

There are a few other projects that scratch the same itch: notably Secor from Pinterest and Kafka's old hadoop-consumer. Although Secor doesn't rely on running Hadoop jobs, it still uses Hadoop's SequenceFile file format; sequence files allow compression and distributed splitting operations, as well as just letting you access the data in a record-oriented way. We'd been using code similar to Kafka's hadoop-consumer for a long time but it was running slowly and didn't do a great job of watching for new topics and partitions.

We wanted something that didn't introduce the cascade of Hadoop dependencies and would be able to run a little more hands-off.

Bifrost

Bifrost is the tool we wanted. It has a handful of configuration options that need to be set (e.g. S3 credentials, Kafka ZooKeeper consumer properties) and will continually monitor Kafka for new topics/partitions and create consumers to archive the data to S3. 

Here's an example configuration file:

Bifrost is written in Clojure and can be built using Leiningen. If you want to try it locally you can just `lein run`, or, for production you can build an uberjar and run:


Data is stored in "directories" in S3: s3://<bucket>/<kafka-consumer-group-id>/<topic>/partition=<partition-id>/0000000.baldr.gz (the filename is the starting offset for that file.

We use our Baldr file format: the first 8-bytes indicate the length of the record, then the following n-bytes represent the record itself, then another 8-byte record length etc. It provides almost all of what we need from SequenceFiles but without the Hadoop dependencies. We have a Clojure implementation but it should be trivial to write in any language. We also compress the whole file output stream with Gzip to speed up uploads and reduce the amount we store on S3.

Happy Kafka archiving!

Introducing Blueshift: Automated Amazon Redshift ingestion from Amazon S3

I’m very pleased to say I’ve just made public a repository for a tool we’ve built to make it easier to ingest data automatically into Amazon Redshift from Amazon S3: https://github.com/uswitch/blueshift.

Amazon Redshift is a wonderfully powerful product, if you’ve not tried it yet you should definitely take a look; I’ve written before about the value of the analytical flow it enables.

However, as nice as it is to consume data from, ingesting data is a little less fun:

  1. Forget about writing raw INSERT statements: we saw individual inserts take on the order of 5 or 6 seconds per record. Far too slow to support our usual stream let alone the flood given we reprocess the historical transaction log.
  2. Loading data from S3 is definitely the right thing to do. And, if you have a purely immutable append-only flow, this is trivial. However, if you need upsert behaviour you’ll have to write it yourself. Whilst not complicated, we have many different systems and teams pushing data into Redshift: each of these have a slightly different implementation of the same thing.
  3. We’ve seen lots of SQLException 1023 errors when we were reprocessing the historical transaction log across many different machines (our transaction log is itself already on S3 stored as lots of large Baldr files); with n machines in the cluster we’d end up with n transactions inserting into the same table.
  4. Redshift performs best when running loads operating over lots of files from S3: work is distributed to the cluster nodes and performed in parallel. Our original solution ended up with n transactions loading a smaller number of files.

Rationale

Blueshift makes life easier by taking away the client’s need to talk directly to Redshift. Instead, clients write data to S3 and Blueshift automatically manages the ingestion into Redshift.

Blueshift is a standalone service written in Clojure (a dialect of Lisp that targets the JVM) that is expected to be deployed on a server. It is configured to watch an S3 bucket; when it detects new data files and a corresponding Blueshift manifest it will perform an upsert transaction for all the files with the additional benefit of being a single import for a large number of files.

Using

You can build and run the application with Leiningen, a Clojure build tool:

The configuration file requires a minimal number of settings:

Most of it is relatively obvious but for the less obvious bits:

  • :key-pattern is a regex used to filter the directories watched within the S3 bucket; this makes it easier to have a single bucket with data from different environments.
  • :telemetry :reporters configures the Metrics reporters; a log reporter is included but we have an extra project (blueshift-riemann-metrics) for pushing data to Riemann.

Once the service is running you just need to write your delimited data files to the S3 bucket being watched and create the necessary Blueshift manifest. Your S3 layout might look something like this:

When Blueshift polls S3 it will pick up that 2 directories exist and create a watcher for each directory (directory-a/foo and directory-b in our example).

When the directory-a watcher runs it will notice there are 2 data files to be imported and a Blueshift manifest: manifest.edn: this is used to tell Blueshift where and how to import the data.

Of note in the example above:

  • :pk-columns is used to implement the merge upsert: rows in the target table (testing in the above example) that exist in our imported data are removed before the load: Redshift doesn’t enforce PK constraints so you have to delete the data first.
  • :options: can contain any of the options that Redshift’s COPY command supports.
  • :data-pattern helps Blueshift identify which files are suitable for being imported.

When the data has been imported successfully the data files are deleted from S3 leaving the Blueshift manifest ready for more data files.

Summary

We’re delighted with Amazon’s Redshift service and have released Blueshift to make it easier for people to optimally ingest data from their applications via S3 without talking to Redshift directly.

I’d love to hear from people if they find Blueshift useful and I’d be especially delighted for people to contribute (it’s currently less than 400 lines of Clojure).

Baldr: a record-oriented file format lib in Clojure

At uSwitch.com we’re in the process of migrating a lot of our data infrastructure to be much lighter-weight: pure Clojure and supporting libraries, rather than sitting atop Hadoop and other chunkier frameworks.

A lot of our activity stream data is currently archived in files on Amazon S3- specifically, Hadoop SequenceFiles; these are record-oriented files that contain key/value pairs suitable for MapReduce-style processing.

Working with SequenceFile formatted files requires a dependency on org.apache.hadoop/hadoop-core and a ton of transitive dependencies. Further, if you’re compressing the contents of the files (Hadoop SequenceFile’s support both record and block compression with Deflate, GZip and Snappy codecs) with GZip or Snappy compression you’ll need the hadoop-native lib which is a real effort/impossible to build on anything but Linux.

Being able to write arbitrary bytes to a file is really useful for serialization, but, we really need message/record boundaries when consuming those records back.

We were convinced that this file format must exist already but couldn’t find anything so we wrote a small library called Baldr for working with records of bytes.

We’re still convinced this kind of thing must already exist somewhere else or at least be called something in particular. But, in the meantime it solves the problem neatly (and with far fewer dependencies).

Significance Irrelevant in Online Experiments

Earlier this week I heard about Google Analytics’ new Content Experiments feature. The help also includes some interesting information about the engine used to run the experiments.

I did some further reading from the references and came across A Modern Bayesian Look at the Multi-armed Bandit by S. L. Scott.

The author of the paper makes a very interesting argument as to why multi-armed bandits suit online experiments better than classic null-hypothesis experiment methods: classic experiments control for the wrong type of error.

In a classical experiment we would randomise people into two groups, expose the two groups to different treatments, and finally collect data to measure the effect. We then use statistics to determine whether we have statistically significant evidence to reject the null hypothesis.

Scott argues that such experiments “are designed to be analyzed using methods that tightly control the type-I error rate under a null hypothesis of no effect”.

Type-I vs. Type-II Error

A Type-I error is the incorrect rejection of a true null hypothesis. That is, picking a treatment that isn’t materially better. In contrast, a Type-II error is “failing to reject a false null hypothesis”. That is, failing to switch to a better treatment.

Scott argues that in experiments with low switching costs, such as in online optimisation, type-II errors are more costly than type-I errors. In other words, it’s far worse for us to fail to try a better treatment than it is to incorrectly pick a treatment that isn’t materially better.

In short Scott states that, for online experiments, “the usual notion of statistical significance [is] largely irrelevant”.

Amazon Redshift + R: Analytics Flow

Ok, so it’s a slightly fanboy-ish title but I’m starting to really like the early experimentation we’ve been doing with Amazon’s Redshift service at uSwitch.

Our current data platform is a mix of Apache Kafka, Apache Hadoop/Hive and a set of heterogenous data sources mixed across the organisation (given we’re fans of letting the right store find it’s place).

The data we ingest is reasonably sizeable (gigabytes a day); certainly enough to trouble the physical machines uSwitch used to host with. However, for nearly the last 3 years we’ve been breaking uSwitch’s infrastructure and systems apart and it’s now much easier to consume whatever resources you need.

Building data systems on immutable principles also makes this kind of experimentation so much easier. For a couple of weeks we (Paul and I) have been re-working some of our data warehousing ETL to see what a Redshift analytics world looks like.

Of course it’s possible to just connect any JDBC SQL client to Redshift but we want to be able to do some more interactive analysis on the data we have. We want an Analytics REPL.

Redshift in R

I’m certainly still a novice when it comes to both statistical analyses and R but it’s something I’m enjoying- and I’m lucky to work with people who are great at both.

R already has a package for connecting to databases using JDBC but I built a small R package that includes both the Postgresql 8.4 JDBC driver and a few functions to make it nicer to interact with: Redshift.R. N.B. this was partly so I could learn about writing R packages, and partly about making it trivial for other R users in the company to get access to our experimental cluster.

The package is pretty easy to install- download the tarball, uncompress and run an R statement. The full instructions are available on the project’s homepage. Once you’ve installed it you’re done- no need to download anything else.

Flow

What I found really interesting, however, was how I found my workflow once data was accessible in Redshift and directly usable from inside my R environment; the 20 minute lead/cycle time for a Hive query was gone and I could work interactively.

I spent about half an hour working through the following example- it’s pretty noddy analytics but shows why I’m starting to get a little excited about Redshift: I can work mostly interactively without needing to break my work into pieces and switch around the whole time.

Disclosure

It would be remiss of me not to mention that R already has packages for connecting to Hadoop and Hive, and work to provide faster querying through tools like Cloudera’s Impala. My epiphany is probably also very old news to those already familiar with connecting to Vertica or Teradata warehouses with ODBC and R. 

The killer thing for me is that it cost us probably a few hundred dollars to create a cluster with production data in, kick the tyres, and realise there’s a much better analytics cycle for us out there. We're really excited to see where this goes.

Kafka for uSwitch's Event Pipeline

Kafka is a high-throughput, persistent, distributed messaging system that was originally developed at LinkedIn. It forms the backbone of uSwitch.com’s new data analytics pipeline and this post will cover a little about Kafka and how we’re using it.

Kafka is both performant and durable. To make it easier to achieve high throughput on a single node it also does away with lots of stuff message brokers ordinarily provide (making it a simpler distributed messaging system).

Messaging

Over the past 2 years we’ve migrated from a monolithic environment based around Microsoft .NET and SQL Server to a mix of databases, applications and services. These change over time: applications and servers will come and go.

This diversity is great for productivity but has made data analytics as a whole more difficult.

We use Kafka to make it easier for the assortment of micro-applications and services, that compose to form uSwitch.com, to exchange and publish data.

Messaging helps us decouple the parts of the infrastructure letting consumers and producers evolve and grow over time with less centralised coordination or control; I’ve referred to this as building a Data Ecosystem before.

Kafka lets us consume data in realtime (so we can build reactive tools and products) and provides a unified way of getting data into long-term storage (HDFS).

Consumers and producers

Kafka’s model is pretty general; messages are published onto topics by producers, stored on disk and made available to consumers. It’s important to note that messages are pulled by consumers to avoid needing any complex throttling in the event of slow consumption.

Kafka doesn’t dictate any serialisation it just expects a payload of byte[]. We’re using Protocol Buffers for most of our topics to make it easier to evolve schemas over time. Having a repository of definitions has also made it slightly easier for teams to see what events they can publish and what they can consume.

This is what it looks like in Clojure code using clj-kafka.

We use messages to record the products that are shown across our site, the searches that people perform, emails that are sent (and bounced), web requests and more. In total it’s probably a few million messages a day.

Metadata and State

Kafka uses Zookeeper for various bits of meta-information, including tracking which messages have already been retrieved by a consumer. To that end, it is the consumers responsibility to track consumption- not the broker. Kafka’s client library already contains a Zookeeper consumer that will track the message offsets that have been consumed.

As an side, the broker keeps no state about any of the consumers directly. This keeps it simple and means that there’s no need for complex structures kept in memory reducing the need for garbage collections.

When messages are received they are written to a log file (well, handed off to the OS to write) named after the topic; these are serial append files so individual writes don’t need to block or interfere with each other.

When reading messages consumers simply access the file and read data from it. It’s possible to perform parallel consumption through partitioned topics although this isn’t something we’ve needed yet.

Topic and message storage

Messages are tracked by their offset- letting consumers access from a given point into the topic. A consumer can connect and ask for all messages that Kafka has stored currently, or from a specified offset. This relatively long retention (compared to other messaging systems) makes Kafka extremely useful to support both real-time and batch reads. Further, because it takes advantage of disk throughput it makes it a cost-effective system too.

The broker can be configured to keep messages up to a specified quantity or for a set period of time. Our broker is configured to keep messages for up to 20 days, after that and you’ll need to go elsehwere (most topics are stored on HDFS afterwards). This characteristic that has made it so useful for us- it makes getting data out of applications and servers and into other systems much easier, and more reliable, than periodically aggregating log files.

Performance

Kafka’s performance (and the design that achieves it) is derived from the observation that disk throughput has outpaced latency; it writes and reads sequentially and uses the operating system’s file system caches rather than trying to maintain its own- minimising the JVM working set, and again, avoiding garbage collections.

The plot below shows results published within an ACM article; their experiment was to measure how quickly they could read 4-byte values sequentially and randomly from different storage.

Performance

Please note the scale is logarithmic because the difference between random and sequential is so large for both SSD and spinning disks.

Interestingly, it shows that sequential disk access, spinning or SSD, is faster than random memory access. It also shows that, in their tests, sequential spinning disk performance was higher than SSD.

In short, using sequential reads lets Kafka get performance close to random memory access. And, by keeping very little in the way of metadata, the broker can be extremely lightweight.

If anyone is interested, the Kafka design document is very interesting and accessible.

Batch Load into HDFS

As I mentioned earlier, most topics are stored on HDFS so that we can maximise the amount of analysis we can perform over time.

We use a Hadoop job that is derived from the code included within the Kafka distribution.

The process looks a little like this:

Hadoop Loading

Each topic has a directory on HDFS that contains 2 further subtrees: these contain offset token files and data files. The input to the Hadoop job is an offset token file which contains the details of the broker to consume from, the message offset to read from, and the name of the topic. Although it’s a SequenceFile the value bytes contain a string that looks like this:

broker.host.com topic-name  102991

The job uses a RecordReader that connects to the Kafka broker and passes the message payload directly through to the mapper. Most of the time the mapper will just write the whole message bytes directly out which is then written using Hadoop’s SequenceFileOutputFormat (so we can compress and split the data for higher-volume topics) and Hadoop’s MultipleOutputs so we can write out 2 files- the data file and a newly updated offset token file.

For example, if we run the job and consume from offset 102991 to offset 918280, this will be written to the offset token file:

broker.host.com topic-name  918280

Note that the contents of the file is exactly the same as before just with the offset updated. All the state necessary to perform incremental loads is managed by the offset token files.

This ensures that the next time the job runs we can incrementally load only the new messages. If we introduce a bug into the Hadoop load job we can just delete one or more of the token files to cause the job to load from further back in time.

Again, Kafka’s inherent persistence makes dealing with these kinds of HDFS loads much easier than dealing with polling for logs. Previously we’d used other databases to store metadata about the daily rotated logs we’d pulled but there was lots of additional computation in splitting apart files that would span days- incremental loads with Kafka are infinitely cleaner and efficient.

Kafka has helped us both simplify our data collection infrastructure, letting us evolve and grow it more flexibly, and provided the basis for building real-time systems. It’s extremely simple and very easy to setup and configure, I’d highly recommend it for anyone playing in a similar space.

Related Stuff

As I publish this LinkedIn have just announced the release of Camus: their Kafka to HDFS pipeline. The pipeline I’ve described above was inspired by the early Hadoop support within Kafka but has since evolved into something specific for use at uSwitch.

Twitter also just published about their use of Kafka and Storm to provide real-time search.

I can also recommend reading “The Unified Logging Infrastructure for Data Analytics at Twitter” paper that was published late last year.

Finally, this post was based on a brief presentation I gave internally in May last year: Kafka a Little Introduction

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Clojure - From Callbacks to Sequences

I was doing some work with a colleague earlier this week which involved connecting to an internal RabbitMQ broker and transforming some messages before forwarding them to our Kafka broker.

We’re using langohr to connect to RabbitMQ. Its consumer and queue documentation shows how to use the subscribe function to connect to a broker and print messages that arrive:

The example above is pretty close to what we started working with earlier today. It’s also quite similar to a lot of other code I’ve written in the past: connect to a broker or service and provide a block/function to be called when something interesting happens.

Sequences, not handlers

Although there’s nothing wrong with this I think there’s a nicer way: flip the responsibility so instead of the subscriber pushing to our handler function we consume it through Clojure’s sequence abstraction.

This is the approach I took when I wrote clj-kafka, a Clojure library to interact with LinkedIn’s Kafka (as an aside, Kafka is really cool- I’m planning a blog post on how we’ve been building a new data platform for uSwitch.com but it’s well worth checking out).

Here’s a little example of consuming messages through a sequence that’s taken from the clj-kafka README:

We create our consumer and access messages through a sequence abstraction by calling messages with the topic we wish to consume from.

The advantage of exposing the items through a sequence is that it becomes instantly composable with the many functions that already exist within Clojure: map, filter, remove etc.

In my experience, when writing consumption code that uses handler functions/callbacks I’ve ended up with code that looks like this:

It makes consuming data more complicated and pulls more complexity into the handler function than necessary.

Push to Pull

This is all made possible thanks to a lovely function written by Christophe Grande:

The function returns a vector containing 2 important parts: the sequence, and a function to put things into that sequence.

Returning to our original RabbitMQ example, we can change the subscriber code to use pipe to return the sequence that accesses the queue of messages:

We can then map, filter and more.

We pull responsibility out of the handler function and into the consumption of the sequence. This is really important, and it compliments something else which I’ve recently noticed myself doing more often.

In the handler function above I convert the function parameters to a map containing :payload, :ch and :msg-meta. In our actual application we’re only concerned with reading the message payload and converting it from a JSON string to a Clojure map.

Initially, we started writing something similar to this:

We have a function that exposes the messages through a sequence, but we pass a kind of transformation function as the last argument to subscriber-seq. This initially felt ok: subscriber-seq calls our handler and extracts the payload into our desired representation before putting it into the queue that backs the sequence.

But we’re pushing more responsibility into subscriber-seq than needs to be there.

We’re just extracting and transforming messages as they appear in the sequence so we can and should be building upon Clojure's existing functions: map and the like. The code below feels much better:

It feels better for a similar reason as moving the handler to a sequence- we’re making our function less complex and encouraging the composition through the many functions that already exist. Line 13 is a great example of this for me- map’ing a composite function to transform the incoming data rather than adding more work into subscriber-seq.

Pipe

I’ve probably used Christophe’s pipe function 3 or 4 times this year to take code that started with handler functions and evolved it to deal with sequences. I think it’s a really neat way of making callback-based APIs more elegant.

Multi-armed Bandit Optimisation in Clojure

The multi-armed (also often referred to as K-armed) bandit problem models the problem a gambler faces when attempting maximise the reward from playing multiple machines with varying rewards.

For example, let’s assume you are standing in front of 3 arms and that you don’t know the rate at which they will reward you. How do you set about the task of pulling the arms to maximise your cumulative reward?

It turns out there’s a fair bit of literature on this topic, and it’s also the subject of a recent O’Reilly book: “Bandit Algorithms for Website Optimization” by John Myles White (who also co-wrote the excellent Machine Learning for Hackers).

This article discusses my implementation of the algorithms which is available on GitHub and Clojars: https://github.com/pingles/clj-bandit.

Enter Clojure

I was happy to attend this year’s clojure-conj and started reading through the PDF whilst on the flight out. Over the next few evenings, afternoons and mornings (whenever I could squeeze in time) I spent some time hacking away at implementing the algorithms in Clojure. It was great fun and I pushed the results into a library: clj-bandit.

I was initially keen on implementing the same algorithms and being able to reproduce the results shown in the book. Since then I’ve spent a little time tweaking parts of the code to be a bit more functional/idiomatic. The bulk of this post covers this transition.

I started with a structure that looked like this:

From there I started layering on functions that select the arm with each algorithm’s select-arm implemented in its own namespace.

One of the simplest algorithms is Epsilon-Greedy: it applies a fixed probability when deciding whether to explore (try other arms) or exploit (pull the currently highest-rewarding arm).

The code, as implemented in clj-bandit, looks like this:

We generate a random number (between 0 and 1) and either pick the best performing or a random item from the arms.

In my initial implementation I kept algorithm functions together in a protocol, and used another protocol for storing/retrieving arm data. These were reified into an ‘algorithm’:

Applying select-arm to the current arm state would select the next arm to pull. Having pulled the arm, update-reward would let the ‘player’ track whether they were rewarded or not.

This worked, but it looked a little kludgey and made the corresponding monte-carlo simulation code equivalently disgusting.

I initially wanted to implement all the algorithms so I could reproduce the same results that were included in the book but the resulting code definitely didn’t feel right.

More Functional

After returning from the conference I started looking at the code and started moving a few functions around. I dropped the protocols and went back to the original datastructure to hold the algorithm’s current view of the world.

I decided to change my approach slightly and introduced a record to hold data about the arm.

The algorithms don’t need to know about the identity of any individual arm- they just need to pick one from the set. It tidied a lot of the code in the algorithms. For example, here’s the select-arm code from the UCB algorithm:

Functional Simulation

The cool part about the book and it’s accompanying code is that includes a simulation suitable for measuring the performance and behaviour of each of the algorithms.

This is important because the bandit algorithms have a complex feedback cycle: their behaviour is constantly changing in the light of data given to them during their lifetime.

For example, following from the code by John Myles White in his book, we can visualise the algorithm’s performance over time. One measure is accuracy (that is, how likely is the algorithm to pick the highest paying arm at a given iteration) and we can see the performance across algorithms over time, and according to their exploration/exploitation parameters, in the plot below:

The simulation works by using a series of simulated bandit arms. These will reward randomly according to a specified probability:

We can model the problem neatly by creating a function representing the arm, we can then pull the arm by applying the function.

As I mentioned earlier, when the code included protocols for algorithms and storage, the simulation code ended up being pretty messy. After I’d dropped those everything felt a little cleaner and more Clojure-y. This felt more apparent when it came to rewriting the simulation harness.

clj-bandit.simulate has all the code, but the key part was the introduction of 2 functions that performed the simulation:

simulation-seq creates a sequence through iterate’ing the simulate function. simulate is passed a map that contains the current state of the algorithm (the performance of the arms), it returns the updated state (based on the pull during that iteration) and tracks the cumulative reward. Given we’re most interested in the performance of the algorithm we can then just (map :result ...) across the sequence. Much nicer than nested doseq’s!

Further Work

At uSwitch we’re interested in experimenting with multi-armed bandit algorithms. We can use simulation to estimate performance using already observed data. But, we’d also need to do a little work to consume these algorithms into web applications.

There are existing Clojure libraries for embedding optimisations into your Ring application:

  1. Touchstone
  2. Bestcase

These both provide implementations of the algorithms and for storing their state in stores like Redis.

I chose to work through the book because I was interested in learning more about the algorithms but I also like the idea of keeping the algorithms and application concerns separate.

Because of that I’m keen to work on a separate library that makes consuming the algorithms from clj-bandit into a Ring application easier. I’m hoping that over the coming holiday season I’ll get a chance to spend a few more hours working on it.

Analysing and predicting performance of our Neo4j Cascading Connector with linear regression in R

As I mentioned in an earlier article, Paul and I have produced a library to help connect Cascading and Neo4j making it easy to sink data from Hadoop with Cascading flows into a Neo4j instance. Whilst we were waiting for our jobs to run we had a little fun with some regression analysis to optimise the performance. This post covers how we did it with R.

I’m posting because it wasn’t something I’d done before and it turned out to be pretty good fun. We played with it for a day and haven’t done much else with it since so I’m also publishing in case it’s useful for others.

We improved the write performance of our library by adding support for batching- collecting mutations into sets of transactions that are batched through Neo4j’s REST API. This improved performance (rather than using a request/response for every mutation) but also meant we needed to specify a chunk size; writing all mutations in a single transaction would be impossible.

There are 2 indepent variables that we could affect to tweak performance: the batch size and the number of simultaneous connections that are making those batch calls. N.B this assumes any other hidden factors remain constant.

For us, running this on a Hadoop cluster, these 2 variables determine the batch size in combination with the number of Hadoop’s reduce or map tasks concurrently executing.

We took some measurements during a few runs of the connector across our production data to help understand whether we were making the library faster. We then produced a regression model from the data and use the optimize function to help identify the sweet spot for our job’s performance.

We had 7 runs on our production Hadoop cluster. We let the reduce tasks (where the Neo4j write operations were occurring) run across genuine data for 5 minutes and measured how many nodes were successfully added to our Neo4j server. Although the cluster was under capacity (so the time wouldn’t include any idling/waiting) our Neo4j server instance runs on some internal virtualised infrastructure and so could have exhibited variance beyond our changes.

The results for our 7 observerations are in the table below:

Test No. Number of Reducers Batch Size Nodes per minute
1 1 10 5304.4
2 4 10 13218.8
3 4 20 13265.636
4 8 5 11289.2
5 8 10 17682.2
6 16 10 20984.2
7 8 20 20201.6

Regression in R

A regression lets us attempt to predict the value of a continuous variable based upon the value of one or more other independent variables. It also lets us quantify the strength of the relationship between the dependent variable and independent variables.

Given our experiment, we could determine whether batch size and the number of reducers (the independent variables) affected the number of Neo4j nodes we could create per minute (the dependent variable). If there was, we would use values for those 2 variables to predict performance.

The first stage is to load the experiment data into R and get it into a data frame. Once we’ve loaded it we can use R’s lm function to fit a linear model and look at our data.

In the above, the formula parameter to lm lets us describe that nodes.per.minute is our dependent variable (our outcome), and reducers and batch.size are our independent variables (our predictors).

Much like other analysis in R, the first thing we can look at is a summary of this model, which produces the following:

lm(formula = nodes.per.minute ~ reducers + batch.size, data = results)

Residuals:
    1     2     3     4     5     6     7 
-2330  2591 -1756 -1135  3062 -1621  1188 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)   2242.8     3296.7   0.680   0.5336  
reducers       998.1      235.6   4.236   0.0133 *
batch.size     439.3      199.3   2.204   0.0922 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 2735 on 4 degrees of freedom
Multiple R-squared: 0.8362, Adjusted R-squared: 0.7543 
F-statistic: 10.21 on 2 and 4 DF,  p-value: 0.02683

The summary data tells us that the model supports the data relatively well. Our R-squared is 0.075 and both batch size and reducer size are considered significant.

But, what if we tweak our model? We suspect that the shape of the performance through increasing reducers and batch size is unlikely to exhibit linear growth. We can change the formula of our model and see whether we can improve the accuracy of our model:

And let’s the the results of calling summary(model):

Call:
lm(formula = nodes.per.minute ~ reducers + I(reducers^2) + batch.size + 
    I(batch.size^2), data = results)

Residuals:
         1          2          3          4          5          6          7 
-2.433e+02  9.318e+02 -3.995e+02  9.663e-13 -7.417e+02  5.323e+01  3.995e+02 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)  
(Intercept)     -15672.16    3821.48  -4.101   0.0546 .
reducers          2755.10     337.07   8.174   0.0146 *
I(reducers^2)     -101.74      18.95  -5.370   0.0330 *
batch.size        2716.07     540.07   5.029   0.0373 *
I(batch.size^2)    -85.94      19.91  -4.316   0.0497 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 948.6 on 2 degrees of freedom
Multiple R-squared: 0.9901, Adjusted R-squared: 0.9704 
F-statistic: 50.25 on 4 and 2 DF,  p-value: 0.01961

Our R-squared is now 0.9704- our second model fits the data better than our first model.

Optimization

Given the above, we’d like to understand the values for batch size and number of reducers that will give us the highest throughput.

R has an optimize function that, given a range of values for a function parameter, returns the optimal argument for the return value.

We can create a function that calls predict.lm with our model to predict values. We can then use the optimize function to find our optimal solution:

We use a set batch size of 20 and optimize to discover that the optimal number of reducers is 13 with a throughput of 22,924 nodes/minute. The second command optimizes for batch size with a fixed number of reducers. Again, it suggests a batch size of 15 for an overall throughput of 24,409 nodes/minute.

This supports what we observed earlier with the summary data: number of reducers is more significant than batch size for predicting throughput.

I’m extremely new to most of R (and statistics too if I’m honest- the last year is the most I’ve done since university) so if anyone could tell me if there’s a way to perform an optimization for both variables that would be awesome.

Please note this post was more about our experimentation and the process- I suspect our data might be prone to systematic error and problems because we only have a few observations. I’d love to run more experiments and get more measurements but we moved on to a new problem :)