34 posts
Hive's atomic data types support implicit conversion, similar to Java's type conversion. If an expression uses INT, for example, a TINYINT is automatically converted to INT — but Hive never converts in the reverse direction.
Last time we learned what Hive is. This post walks you through installing and deploying Hive so you can try this legendary data warehouse yourself.
Having finished the Hadoop beginner series, we're finally getting to the real data warehouse work. Let's start by understanding what Hive is in the Hadoop ecosystem.
The first three ZooKeeper tutorials left you able to use it. This post goes a step further and gives you a general idea of how ZooKeeper actually works.
Last time we could already drive ZooKeeper from code and learned about registering watchers. Time to try it out: this post walks through a simple hands-on example that watches a node for changes.
Last post got ZooKeeper installed. The command line works, but most of the time you operate ZooKeeper through API calls, so this post covers the basic beginner-level operations.
ZooKeeper is a distributed, open-source coordination service for distributed applications, an open-source implementation of Google's Chubby, and a key component of Hadoop and HBase. It accepts watchers' registrations and notifies them when data changes — think of it as ZooKeeper = filesystem + notification mechanism.
The previous tutorial introduced how MapReduce executes and sketched out InputFormat. Now it's time to talk about Shuffle. The data processing that happens after the Map method and before the Reduce method is called Shuffle.
In the previous post we got hands-on with MapReduce by writing some code. The MapReduce workflow looks roughly like this.
Last session we went through using MapReduce from code, where we met Writable classes for the first time. This section covers roughly what they're for.
