I’ll preface this by saying that I know just enough about machine learning to be dangerous and get myself into trouble. That said, if anything is inaccurate or misleading let me know in the comments and I’ll update it. Last April Amazon announced Amazon Machine Learning, a new AWS service aimed at developers to help them build and deploy machine learning solutions. We’ve been excited to experiment with AWS ML since it launched but haven’t had a chance until just now.
So what is “machine learning”? Looking at Wikipedia’s definition machine learning is ‘is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a “Field of study that gives computers the ability to learn without being explicitly programmed”.’ That definition in turn translates to using a computer to solve problems like regression or classification. Machine learning powers dozens of the products that internet users interact with everyday from spam filtering to product recommendations to Siri and Google Now.
Looking at the Wikipedia article, ML as a field has existed since the late 1980s so what’s been driving its recent growth in popularity? I’d argue key driving factors have been compute resources getting cheaper, especially storage, which has allowed companies to store orders of magnitude more data than they were 5 or 10 years ago. This data along with elastic public cloud resources and the increasing maturity of open source packages has made ML accessible and worthwhile for an increasingly large number of companies. Additionally, there’s been an explosion of venture capital funding into ML focussed startups which has certainly also helped boost its popularity.
The first thing we need to do before testing out Amazon ML was to pick a good machine learning problem to tackle. Unfortunately, we didn’t have any internal data to test with so I headed over to Kaggle to find a good problem to tackle. After some exploring I settled on Digit Recognizer since its a “known problem”, the Kaggle challenge had benchmark solutions, and no additional data transformations would be neccessary. The goal of the Digit Recognizer problem is to accept bitmap representations of handwritten numerals and then correctly output what number was written.
The dataset is a modified version of the Mixed National Institute of Standards and Technology which is a well known dataset often used for training image processing systems. Unlike the original MNIST images, the Kaggle dataset has already been converted to a grayscale bitmap array so individual pixels are represented by an integer from 0-255. In ML parlance, the “Digit Recognizer” challenge would fall under the umbrella of a classification problem since the goal would be to correctly “classify” unknown inputs with a label, in this case a 0-9 digit. Another interesting feature of the MNIST dataset is that the Wikipedia provides benchmark performance for a variety of approaches so we can have a sense of how AWS ML stacks up.
At a high level, the big steps we’re going to take are to train our model using “train.csv”, evaluate it against a subset of known data, and then predict labels for the rows in “test.csv”. Amazon ML makes this whole process pretty easy using the AWS Console UI so there’s not really any magic. One thing worth noting is that Amazon doesn’t let you select which algorithm will be used in the model you build, it selects it automatically based on the type of ML problem. After around 30 minutes your model should be built and you’ll be able to explore the model’s performance. This is actually a really interesting feature of Amazon ML since you wouldn’t get these insights with visualizations “out of the box” from most open source packages.
With the model built the last step is to use it to predict unknown values from the “test.csv” dataset. Similar to generating the model, running a “batch prediction” is pretty straightforward on the AWS ML UI. After the prediction finishes you’ll end up with a results file in your specified S3 bucket that looks similar to:
Because there are several possible classifications of a digit the ML model generates a probability per classification with the largest number being the most likely. Individual probabilities are great but what we really want is a single digit per input sample. Running the input through the following PHP will produce that along with a header for Kaggle:
And finally the last step of the evaluation is uploading our results file to Kaggle to see how our model stacks up. Uploading my results produced a score of 0.91671 so right around 92% accuracy. Interestingly, looking at the Wikipedia entry for MNIST a 8% error rate is right around what was academically achieved using a linear classifier. So overall, not a bad showing!
Comparing the model’s performance to the Kaggle leaderboard and Wikipedia benchmarks, AWS ML performanced decently well especially considering we took the defaults and didn’t pre-process the data. One of the downside of AWS ML is the lack of visibility into what algorithms are being used and additionally not being able to select specific algorithms. In my experience, solutions that mask complexity like this work great for “typical” use cases but then quickly breakdown for more complicated tasks. Another downside of AWS ML is that it can currently only process text data that’s formatted into CSVs with one record per row. The result of this is that you’ll have to do any data transformations with your own code running on your own compute infrastructure or AWS EC2.
Anyway, all in all I think Amazon’s Machine Learning product is definitely an interesting addition to the AWS suite. At the very least, I can see it being a powerful tool to be able to quickly test out ML hypothesis which can then be implemented and refined using an open source package like skit-learn or Apache Mahout.
Over the last few weeks we’ve been working with one of our clients to build out a real time data processing application. At a high level, the system ingests page view data, processes it in real time, and then ingests it into a database backend. In terms of scale, the system would need to start off processing roughly 30,000 events per minute at peak with the capability to scale out to 100,000 events per minute fairly easily. In addition, we wanted the data to become available to query “reasonably quickly” so that we could iterate quickly on how we were processing data.
To kick things off, we began by surveying the available tools to ingest, process, and then ultimately query the data. On the datawarehouse side, we had already had some positive experiences with Amazon Redshift so it was a natural choice to keep using it moving forward. In terms of ingestion and processing, we decided to move forward with Kinesis and Gearman. The fully managed nature of Kinesis made it the most appealing choice and Gearman’s strong PHP support would let us develop workers in a language everyone was comfortable with.
Our final implementation is fairly straightforward. An Elastic Load Balancer handles all incoming HTTP requests which are routed to any number of front end machines. These servers don’t do any computation and fire of messages into a Kinesis stream. On the backend, we have a consumer per Kinesis stream shard that creates Gearman jobs for pre-processing as well as Redshift data ingestion. Although it’s conceptually simple, there’s a couple of “gotchas” that we ran into implementing this system:
Slow HTTP requests are a killer: The Kinesis API works entirely over HTTP so anytime you want to “put” something into the stream it’ll require a HTTP request. The problem with this is that if you’re making these requests in real time in a high traffic environment you run the risk of locking up your php-fpm workers if the network latency to Kinesis starts to increase. We saw this happen first hand, everything would be fine and then all of a sudden the latency across the ELB would skyrocket when the latency to Kinesis increased. To avoid this, you need to make the Kinesis request in the background.
SSL certificate verification is REALLY slow: Kinesis is only available over HTTPs so by default the PHP SDK (I assume others as well) will perform an SSL key verification every time you use a new client. If you’re making Kinesis requests inside your php-fpm workers that means you’ll be verifying SSL keys on every request which turns out to be really slow. You can disable this in the official SDK using the “curl.options” parameter and passing in “CURLOPT_SSL_VERIFYHOST” and “CURLOPT_SSL_VERIFYPEER”
There’s no “batch” add operation: Interestingly Apache Kafka, which Kinesis is based on, supports batch operations but unfortunately Kinesis doesn’t. You have to make an HTTP request for every message you’re adding to the stream. What this means is that even if you’re queuing the messages in the background, you’ll still need to loop through them all firing off HTTP requests
Your consumer needs to be fast: In your consumer, you’ll basically end up with code that looks like – https://gist.github.com/adatta02/842531b3fe93097ee030 Because Kinesis shard iterators are only valid for 5 minutes, you’ll need to be cognizant of how long the inner for loop takes to run. Each “getRecords” call can return a max of 10,000 records so you’ll need to be able to process 10k records in less than 5 minutes. Our solution for this was to offload all the actual processing to Gearman jobs.
Anyway, we’re still fairly new to using Kinesis so I’m sure we’ll learn more about using it as the system is in production. A few things have already been positive including that it makes testing new code locally easy since you can just “tap” into the stream, scaling up looks like it means just adding additional shards, and since its managed we’ve got one less thing to worry about.
As always, questions and comments welcome!
In the last few months there’s been a handful blog posts basically themed “Redshift vs. Hive”. Companies from Airbnb to FlyData have been broadcasting their success in migrating from Hive to Redshift in both performance and cost. Unfortunately, a lot of casual observers have interpreted these posts to mean that Redshift is a “silver bullet” in the big data space. For some background, Hive is an abstraction layer that executes MapReduce jobs using Hadoop across data stored in HDFS. Amazon’s Redshift is a managed “petabyte scale” data warehouse solution that provides managed access to a ParAccel cluster and exposes a SQL interface that’s roughly similar to PostgreSQL. So where does that leave us?
From the outside, Hive and Redshift look oddly similar. They both promise “petabyte” scale, linear scalability, and expose an SQL’ish query syntax. On top of that, if you squint, they’re both available as Amazon AWS managed services through Elastic Mapreduce and of course Redshift. Unfortunately, that’s really where the similarities end which makes the “Hive vs. Redshift” comparisons along the lines of “apples to oranges”. Looking at Hive, its defining characteristic is that it runs across Hadoop and works on data stored in HDFS. Removing the acronym soup, that basically means that Hive runs MapReduce jobs across a bunch of text files that are stored in a distribued file system (HDFS). In comparison, Redshift uses a data model similar to PostgreSQL so data is structured in terms of rows and tables and includes the concept of indexes.
Well therein lays the rub that everyone seem to be missing. Hadoop, and by extension Hive (and Pig) are really good at processing text files. So imagine you have 10 million x 1mb XML documents or 100GB worth of nginx logs, this would be a perfect use case for Hive. All you would have to do is push them into HDFS or S3, write a RegEx to extract your data and then query away. Need to add another 2 million documents or 20GB of logs? No problem, just get them into HDFS and you’re good to go.
Could you do this with Redshift? Sure, but you’d need to pre-process 10 million XML documents and 100GB of logs to extract the appropriate fields, and then create CSV files or SQL INSERT statements to load into Redshift. Given the available options, you’re probably going to end up using Hadoop to do this anyway.
Where Redshift is really going to excel is in situations where your data is basically already relational and you have a clear path to actually get it into your cluster. For example, if you were running three x 15GB MySQL databases with unique, but related data, you’d be able to regularly pull that data into Redshift and then ad-hoc query it with regular SQL. In addition, since the data is already structured you’d be able to use the existing format to create keys in Redshift to improve performance.
When it comes down it, it’ll come down to the old “right tool for the right job” aphorism. As an organization, you’ll have to evaluate how your data is structured, the types of queries you’re interested in running, and what level of abstraction you’re comfortable with. What’s definitely true is that “enterprise” data warehousing is being commoditized and the “old guard” better innovate or die.
Recently we were getting ready to deploy a new project which functions only over SSL. The project is deployed on AWS using the Elastic Load Balancers (ELB). We have the ELB doing the SSL termination to reduce the load on the server and to help simply management of the SSL certs. Anyways the the point of this short post. One of the developers noticed that on some of the internal links she kept getting a link something like “https://dev.app.com:80/….”, it was properly generating the link to HTTPS but then specify port 80. Of course your browser really does not like that as its conflicting calls of port 80 and 443. After a quick look into the project we found that we had yet to enable the proxy headers and specify the proxy(s), it was we had to turn on `trust_proxy_headers`. However, doing this did not fix the issue. You must in addition to enable the headers specify which ones you trust. This can be easily done via the following:
Here is a very simple example of how you could specify them. You just let it know the IP’s of the proxy(s) and it will then properly generate your links.
You can read up on this more in the Symfony documentation on trusting proxies.
Anyways just wanted to put throw this out there incase you see this and realize you forgot to configure the proxy in your app!
We use Amazon Web Services quite a bit here. We not only use it to host most of our clients’ applications, but also for backups. We like to use S3 to store our backups as it is reliable, secure and very cheap. S3 stands for Amazon’s Simple Storage Service, it is more or less a limitless place to store data. You can mount S3 as a network hard drive but it’s main use is to store objects, or data, that you can retrieve at a low cost. It has 99.999999999% durability, so you most likely won’t lose anything, but even if you do, we use produce multiple backups for every object.
One thing we’ve noticed is that some people have issues interacting with S3, so here are a few things to help you out. First, if you are just looking to browse your S3 you can do so via your AWS Console or I like to use S3Fox. However, when you are looking to write some scripts or access it from the command line it can be difficult if you don’t use some pre-built tools. The best one we’ve found is s3cmd.
s3cmd allows you to list, update, create, delete objects and buckets in your S3. It’s really easy to install. Depending on your distribution of linux you can most likely get it from your package manager. Once you’ve done that you can configure it easily via ‘s3cmd –configure’. You’ll just need access credentials from your AWS account. Once you’ve set it up lets go through some useful commands.
To list your available buckets:
To create a bucket:
To list the contents of a bucket:
To put a file in the bucket it is very easy, just run (ie move tester-1.jpg to the bucket):
To delete the file you can run:
These are the basics. Probably the most common uses that we see are doing backups of data from a server to S3. An example of a bash script for this is as follows:
In this script it will just output the the console any errors. As you are most likely not running this by hand every day you’d want to change the “echo” statements to be mail commands or another way to alert administrators of an error on the backup. If you want to backup more than once a day all you need to change is the way the SQL_FILE variable is named to include hours for example.
This is a very simple backup script for MySQL. One thing that it doesn’t do is remove any old files, there is no reason for this to happen in the script. Amazon now has object lifecycles which allows you to automatically expire files in a bucket that are older than 60 days for example.
One thing that many people forget to do when they are making backups is to make sure that they actually work. We highly suggest that you once a month have a script which will check that whatever you are backing up is valid. This means if you are backing up a database that it checks to make sure that the database will reimport and that the data is valid (ie a row that should always exist does). The worst thing is finding out when you need a backup that your backup failed ages ago and you have no valid ones.
Make sure that your backups are not deleted quicker than it would take you to discover a problem. For example, if you only check your blog once a week, don’t have your backups delete after 5 days as you may discover a problem too late and your backups will also have the problem. Storage is cheap, keep backups for a long time.
Hope s3cmd makes your life easier and if you have any questions leave us a comment below!