Hive joins are executed by MapReduce jobs through different execution engines like for example Tez, Spark or MapReduce. Joins even of multiple tables can be achieved by one job only. Since it’s first release many optimizations have been added to Hive giving users various options for query improvements of joins.
With the release of Hive 0.13.1 and HCatalog, a new Streaming API was released as a Technical Preview to support continuous data ingestion into Hive tables. This API is intended to support streaming clients like Flume or Storm to better store data in Hive, which traditionally has been a batch oriented storage.
Based on the newly given ACID insert/update capabilities of Hive, the Streaming API is breaking down a stream of data into smaller batches which get committed in a transaction to the underlying storage. Once committed the data becomes immediately available for other queries.
Broadly speaking the API consists of two parts. One part is handling the transaction while the other is dealing with the underlying storage (HDFS). Transactions in Hive are handled by the the Metastore. Kerberos is supported from the beginning!
Some of the current limitations are:
- Only delimited input data and JSON (strict syntax) are supported
- Only ORC support
- Hive table must be bucketed (unpartitioned tables are supported)
In this post I would like to demonstrate the use of a newly created Storm HiveBolt that makes use of the streaming API and is quite straightforward to use. The source of the here described example is provided at GitHub. To run this demo you would need a HDP 2.2 Sandbox, which can be downloaded for various virtualization environments here. Continue reading “Hive Streaming with Storm”
RHadoop is probably one of the best ways to take advantage of Hadoop from R by making use of Hadoop’s Streaming capabilities. Another possibility to make R work with Big Data in general is the use of SQL with for example a JDBC connector. For Hive there exists such a possibility with the Hive Server 2 Client JDBC. In combination with UDFs this has the potential to be quite a powerful approach to leverage the best of the two. In this post I would like to demonstrate the preliminary steps necessary to make R and Hive work.
If you have the Hortonworks Sandbox setup you should be able to simply follow along as you read. If not you probably are able to adapt where appropriate. First we’ll have to install R on a machine with access to Hive. By default this means the machine should be able to access port 1000 or 1001 where the Hive server is installed. Next we are going to use a sample table in Hive to query from R setting up all required packages.
ORC (Optimized Row Columnar) is a columnar file format optimized to improve performance of Hive. Through the Hive metastore and HCatalog reading, writing, and processing can also be accomplished by MapReduce, Pig, Cascading, and so on. It is very similar to Parquet which is being developed by Cloudera and Twitter. Both are part of the most current Hive release and available to be used immediately. In this post I would like to describe some of the key concepts of ORC and demonstrate how to get started quickly using HCatalog. Continue reading “Getting Started with ORC and HCatalog”