HBase Compaction

Kevin Feasel

2017-03-03

Hadoop

Jitendra Bafna explains how HBase compaction works:

Compaction is a process by which HBase cleans itself. It comes in two flavors: minor compaction and major compaction.

Minor compaction is the process of combining the configurable number of smaller HFiles into one Large HFile. Minor compaction is very important because without it, reading particular rows requires many disk reads and can reduce overall performance.

Major compaction is a process of combining the StoreFiles of regions into a single StoreFile. It also deletes remove and expired versions. By default, major compaction runs every 24 hours and merges all StoreFiles into single StoreFile. After compaction, if the new larger StoreFile is greater than a certain size (defined by property), the region will split into new regions.

Read on for more information about compaction and data locality, which is a totally different topic.

Related Posts

Building TensorFlow Neural Networks On Spark With Keras

Jules Damji has an example of using the PyCharm IDE to use Keras to build TensorFlow neural network models on the Databricks MLflow library: Our example in the video is a simple Keras network, modified from Keras Model Examples, that creates a simple multi-layer binary classification model with a couple of hidden and dropout layers and […]

Read More

Hortonworks Data Platform 3.0 Released

Saumitra Buragohain, et al, announce the newest version of the Hortonworks Data Platform: Highlighted Apache Hive features include: Workload management for LLAP:  You can assign resource pools within LLAP pool and allocate resources on a per user or per group basis. This enables support for large multi-tenant deployments. ACID v2 and ACID on by default:  We are […]

Read More

Categories

March 2017
MTWTFSS
« Feb Apr »
 12345
6789101112
13141516171819
20212223242526
2728293031