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HBase
HBase

200
164
+ 1
12
RocksDB
RocksDB

47
56
+ 1
10
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HBase vs RocksDB: What are the differences?

What is HBase? The Hadoop database, a distributed, scalable, big data store. Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.

What is RocksDB? Embeddable persistent key-value store for fast storage, developed and maintained by Facebook Database Engineering Team. RocksDB is an embeddable persistent key-value store for fast storage. RocksDB can also be the foundation for a client-server database but our current focus is on embedded workloads. RocksDB builds on LevelDB to be scalable to run on servers with many CPU cores, to efficiently use fast storage, to support IO-bound, in-memory and write-once workloads, and to be flexible to allow for innovation.

HBase and RocksDB can be categorized as "Databases" tools.

"Performance" is the top reason why over 7 developers like HBase, while over 2 developers mention "Very fast" as the leading cause for choosing RocksDB.

HBase and RocksDB are both open source tools. It seems that RocksDB with 14.1K GitHub stars and 3.09K forks on GitHub has more adoption than HBase with 2.87K GitHub stars and 1.98K GitHub forks.

According to the StackShare community, HBase has a broader approval, being mentioned in 54 company stacks & 18 developers stacks; compared to RocksDB, which is listed in 6 company stacks and 7 developer stacks.

What is HBase?

Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.

What is RocksDB?

RocksDB is an embeddable persistent key-value store for fast storage. RocksDB can also be the foundation for a client-server database but our current focus is on embedded workloads. RocksDB builds on LevelDB to be scalable to run on servers with many CPU cores, to efficiently use fast storage, to support IO-bound, in-memory and write-once workloads, and to be flexible to allow for innovation.
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      What are some alternatives to HBase and RocksDB?
      Cassandra
      Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
      MongoDB
      MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
      Hadoop
      The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
      Druid
      Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.
      Apache Hive
      Hive facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage.
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      Decisions about HBase and RocksDB
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      How developers use HBase and RocksDB
      Avatar of Pinterest
      Pinterest uses HBaseHBase

      The final output is inserted into HBase to serve the experiment dashboard. We also load the output data to Redshift for ad-hoc analysis. For real-time experiment data processing, we use Storm to tail Kafka and process data in real-time and insert metrics into MySQL, so we could identify group allocation problems and send out real-time alerts and metrics.

      Avatar of Axibase
      Axibase uses HBaseHBase
      • Raw storage engine
      • Replication
      • Fault-tolerance
      Avatar of Mehdi TAZI
      Mehdi TAZI uses HBaseHBase

      Range scan and HDFS Buffering system

      Avatar of anerudhbalaji
      anerudhbalaji uses HBaseHBase

      Primary datastore

      How much does HBase cost?
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