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Editorial Team
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Asked: 2026-06-15T13:50:12+00:00

From my understanding rows are inserted into HBase tables and are getting stored as

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From my understanding rows are inserted into HBase tables and are getting stored as regions in different region server. So, the region server stores the data

Similarly in terms of Hadoop, data is stored in the data nodes present in the hadoop cluster.

Lets say that i have HBase 0.90.6 configured on top of Hadoop 1.1.1 as follows

2 nodes – master and slave

  1. Master node acts as,
    • Hadoop – Namenode, Secondary Namenode, job tracker, data node, task tracker
    • HBase – Master, RegionServer and zookeeper.
  2. Slave node acts as,
    • Hadoop datanode and task tracker
    • HBase region server

Based on my statement if table data is stored in the region servers; then what is the role of the data nodes and region servers?

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  1. Editorial Team

    Data nodes store data. Region server(s) essentially buffer I/O operations; data is permanently stored on HDFS (that is, data nodes). I do not think that putting region server on your ‘master’ node is a good idea.

    Here is a simplified picture of how regions are managed:

    You have a cluster running HDFS (NameNode + DataNodes) with replication factor of 3 (each HDFS block is copied into 3 different DataNodes).

    You run RegionServers on the same servers as DataNodes. When write request comes to RegionServer it first writes changes into memory and commit log; then at some point it decides that it is time to write changes to permanent storage on HDFS. Here is were data locality comes into play: since you run RegionServer and DataNode on the same server, first HDFS block replica of the file will be written to the same server. Two other replicas will be written to, well, other DataNodes. As a result RegionServer serving the region will almost always have access to local copy of data.

    What if RegionServer crashes or RegionMaster decided to reassign region to another RegionServer (to keep cluster balanced)? New RegionServer will be forced to perform remote read first, but as soon as compaction is performed (merging of change log into the data) – new file will be written to HDFS by the new RegionServer, and local copy will be created on the RegionServer (again, because DataNode and RegionServer runs on the same server).

    Note: in case of RegionServer crash, regions previously assigned to it will be reassigned to multiple RegionServers.

    Good reads:

    • Tom White, “Hadoop, The Definitive Guide” has good explanation of HDFS architecture. Unfortunately I did not read original Google GFS paper, so I cannot tell if it is easy to follow.

    • Google BigTable article. HBase is implementation of Google BigTable, and I found that architecture description in this article is the easiest to follow.

    Here is nomenclature differences between Google Bigtable and HBase implementation (from Lars George, “HBase, The Definitive Guide”):

    • HBase – Bigtable
    • Region – Tablet
    • RegionServer – Tablet server
    • Flush – Minor compaction
    • Minor compaction – Merging compaction
    • Major compaction – Major compaction
    • Write ahead log – Commit log
    • HDFS – GFS
    • Hadoop MapReduce – MapReduce
    • MemStore – memtable
    • HFile – SSTable
    • Zookeeper – Chubby
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