A weeks ago, Facebook released a new open source project called PrestoDB which they billed as a market improvement over Hive and Hadoop. According to the PrestoDB site, Presto is a real time query engine that supports a SQL like syntax, similar to Hive. However, unlike Hive, Presto doesn’t execute queries using MapReduce jobs but instead uses its own internal distribution mechanism. According to the Presto site and current users, most queries will see an order of magnitude speedup compared to Hive. And the best part? PrestoDB can read metadata from Hive’s metastore and read files off HDFS just like Hive – pretty wild.
Anyway, since I love new toys (who doesn’t!?) I decided to try setting up PrestoDB on Amazon EMR to see how difficult it was and also experience the speedups. Turns out, once you have an Amazon EMR cluster running getting PrestoDB up is almost trivial. Just follow the PrestoDB deploying directions to get yourself situated. Make sure you create *all* the files or you’ll get some necessarily cryptic errors along the way.
The config files I ended up using were:
You’ll need to create the “/mnt/presto” directory and also make it accessible to whatever user you plan to run the daemon under.
The one huge gotcha I ran into was that I couldn’t figure out what port Hive’s Thrift service was running on. For some reason, it’s notably absent from Amazon’s documentation and I couldn’t find the hive-site.xml file on the EMR EC2. Completely randomly, I ran across this manual page from Jaspersoft enumerating which ports different versions of Hive run Thrift on when you use EMR. Turns out, its different per Hive version but 0.11.0 will use 10004.
Once you have everything configured, just follow the docs to start the server and you’ll be ready to query. One thing to note though is that you’ll need to setup PrestoDB manually on the rest of your machines and also enable the discovery service for this to “really” work.
Anyway, happy querying!
Posted In: Big Data
As far as “big data” solutions go, Hive is probably one of the more recognizable names. Hive basically offers the end user an abstraction layer to run “SQL like” queries as MapReduce jobs across data that they have in HDFS. Concretely, say you had several hundred million rows of data and you wanted to count the number of unique IDs Hive would let you do that. One of the issues with Hadoop and by proxy Hive is that it’s notably difficult to setup a cluster to try things out. Tools like Whirr exist to make things easier they’re, a bit rough around the edges and in my experience hit up against “version hell”. One alternative that I’m surprised isn’t more popular is using Amazon’s Elastic Map Reduce to bootstrap a Hadoop cluster to experiment with.
The first thing you’ll need to do is fire up an EMR cluster from the AWS backend. It’s mostly just point and click but the settings I used were:
The “security and access” section is important, you need to select an existing key pair that you have access to so that you can SSH into your master node to use the Hive CLI client.
Then finally, under Steps since you’re not specifying any pre-determined steps make sure you mark “Auto-terminate” as “No” so that the cluster doesn’t terminate immediately after it boots.
Click “Create Cluster” and you’re off to the races.
Once the cluster launches, you’ll see a dashboard screen with a bunch of information about the cluster including the public DNS address for the “Master”. SSH into this machine using the user “hadoop” and whatever key you launched the cluster with:
Once you’re in, you’ll want to grab some data to play with. I pulled down Wikipedia Page View data since it’s just a bunch of gzipped text files which are perfect for Hive. You can pull down a chunk of files using wget, be aware though that the small EC2s don’t have much storage so you’ll need to keep an eye on your disk space.
Once you have some data (grab a few GB), the next step is to push it over to HDFS, Hadoop’s distributed filesystem. As an aside, Amazon EMR is tightly integrated with Amazon S3 so if you already have a dataset in S3 you can copy directly from S3 to HDFS. Anyway, to push your files to HDFS just run:
And finally, it’s time to query some of the pageview data using Hive. The first step is to let Hive know about your data and what format it’s stored in. To do this, you need to create an external table that points to the location of the files that you just pushed to HDFS. Start the Hive client by running “hive” and then do the following:
Now select some data from your newly created table!
Pretty sweet huh? Now feel free to run any arbitrary query against the data. Note: since we used m1.small EC2s the performance of Hive/Hadoop is going to be pretty abysmal. But hey, give it a shot:
Anyway, don’t forget to tear down the cluster once you’re done. As always, let me know if you run into any issues!
Posted In: Big Data
We’ve been using Hive a bit lately to help clients tackle some of their data needs and without a doubt one of the most powerful features is Hive’s SerDe functionality. Taking a step back, Hive is an open source Apache project that lets you run “SQL Like” queries using Hadoop on data that you have in HDFS. It’s a lot of moving pieces but what it fundamentally comes down to is that Hive will let you run what look like SQL queries across the text files that you have in HDFS. A typical use case would be using Hive to run ad-hoc queries across web server (like nginx) logs. Want to a breakdown of response times by frontend web server? Hive would let you do that.
SerDe is actually short for Serialize/Deserialize and its the mechanism that Hive uses to make sense of your text files in HDFS. Lets take a typical nginx log line:
Now the magic comes in how Hive uses a SerDe to translate a line like that into something that’s queryable. This is contrived but lets assume that for some reason we’re interested in querying on the client IP address and the request size of each log line. So we’d be interested in creating a table that looks like:
Turns out, Hive makes this particularly easy. You’d end up using the RegexSerDe to match a regular expression and then extract the two fields you’re interested in.
The next step after extraction is to do some transformation during the extraction stage and this is where the custom SerDe comes in. For example, lets say that you wanted to geocode the client’s IP address and also convert your dates into Unix timestamps. So your table would be something like:
Your custom SerDe would let you do exactly this. You’d be able to use something like the MaxMind database to geocode your IP addresses and then use some extra Java to convert your timestamps.
Unfortunately, there doesn’t seem to be too much documentation on how to actually write a custom class so here’s a couple of tidbits I’ve picked up:
The best way I’ve found to bootstrap this is to create an Eclipse project, include the necessary JARs, and then get the RegExSerDe to build inside the project. Once that works, test the JAR by creating a table using it and then you’ll be able to modify the class from there.
Even with my awful Java, the RegexSerDe class was easy enough to grok and then modify as needed.
Drop me a comment or shoot me an email and I’ll do my best to help you out.
Posted In: Big Data