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HBase vs RocksDB: What are the differences?


HBase and RocksDB are both popular distributed database systems, but they have several key differences that differentiate them from each other. In this document, we will explore the specific differences between HBase and RocksDB.

  1. Data Model: HBase is a column-oriented database that organizes data in tables and uses a row-key based data model. Each row consists of multiple columns, and columns can be grouped into column families. RocksDB, on the other hand, is a key-value store that stores data in a sorted key-value format. It does not have a concept of tables or rows like HBase.

  2. Storage Layer: HBase is built on top of the Hadoop Distributed File System (HDFS) and stores its data in a distributed manner across multiple nodes. It provides fault-tolerance and high availability by replicating data to multiple nodes. RocksDB, on the other hand, is a local storage engine that stores its data on local disks or SSDs. It does not provide built-in fault-tolerance or replication capabilities like HBase.

  3. Scalability: HBase is designed to scale horizontally by adding more nodes to the cluster. It automatically partitions and distributes data across the cluster, allowing for high scalability. RocksDB, on the other hand, is designed to be a single-node database and does not natively support horizontal scalability. To achieve scalability with RocksDB, users need to shard their data across multiple instances manually.

  4. Consistency Model: HBase provides strong consistency guarantees and supports ACID transactions. It ensures that updates to data are atomic and isolated. RocksDB, on the other hand, is an eventually consistent database that prioritizes high write performance. It does not provide strong consistency guarantees, and updates to data may take some time to propagate across different replicas.

  5. Data Access Patterns: HBase is optimized for random reads and writes and is well-suited for applications that require low latency access to individual records. It provides efficient row-level access and supports complex querying using filters and scans. RocksDB, on the other hand, is optimized for sequential reads and writes and is well-suited for applications that process large amounts of data in batches. It provides efficient range queries and supports iterators for iterating over keys in sorted order.

  6. Data Durability: HBase provides durability guarantees by persisting data to disk and replicating it across multiple nodes. It also provides mechanisms for data backup and disaster recovery. RocksDB, on the other hand, relies on the underlying storage media for data durability. It does not provide built-in mechanisms for data replication or backup.

In summary, HBase and RocksDB differ in their data models, storage layers, scalability, consistency models, data access patterns, and data durability.

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I am researching different querying solutions to handle ~1 trillion records of data (in the realm of a petabyte). The data is mostly textual. I have identified a few options: Milvus, HBase, RocksDB, and Elasticsearch. I was wondering if there is a good way to compare the performance of these options (or if anyone has already done something like this). I want to be able to compare the speed of ingesting and querying textual data from these tools. Does anyone have information on this or know where I can find some? Thanks in advance!

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You've probably come to a decision already but for those are some resources we put together to help people learn more about Milvus and other databases and I don't think they include RocksDB or HBase yet (you could could recommend on GitHub) but hopefully they help answer your Elastic Search questions.

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Pros of HBase
Pros of RocksDB
  • 9
  • 5
  • 1
    Fast Point Queries
  • 5
    Very fast
  • 3
    Made by Facebook
  • 2
    Consistent performance
  • 1
    Ability to add logic to the database layer where needed

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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 companies use HBase?
What companies use RocksDB?
See which teams inside your own company are using HBase or RocksDB.
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What are some alternatives to HBase and RocksDB?
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.
Google Cloud Bigtable
Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.
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.
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 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.
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