Citus vs HBase

Overview

HBase

Stacks511

Followers498

Votes15

GitHub Stars5.5K

Forks3.4K

Citus

Stacks60

Followers124

Votes11

GitHub Stars12.0K

Forks736

Citus vs HBase: What are the differences?

Data Model: Citus is an extension to PostgreSQL that scales out PostgreSQL across multiple nodes, while HBase is a distributed, non-relational database for large-scale, real-time read/write access to data. Citus utilizes a relational data model, making it easier for users familiar with SQL to work with, while HBase uses a wide-column store data model, which allows for dynamic column creation but requires a different approach to data manipulation compared to SQL databases.
Partitioning: Citus uses sharding to distribute data across multiple nodes based on a shard key, allowing for parallel processing, while HBase uses region servers to divide tables into regions based on row keys, providing automatic load balancing and failover capabilities.
Consistency Model: Citus maintains strong consistency by supporting multi-row transactions across distributed data, ensuring all nodes provide the same data with each request, while HBase offers eventual consistency, where updates are propagated to all nodes eventually, leading to potential inconsistencies for a short period.
Query Language: Citus supports SQL queries due to its PostgreSQL compatibility, making it easier for SQL users to transition, while HBase uses HBase Shell or APIs, such as Java API or REST, for data manipulation, which may require a steeper learning curve for users unfamiliar with these interfaces.
Secondary Indexes: Citus provides support for secondary indexes, allowing faster access to data based on non-primary key attributes, while HBase lacks built-in secondary indexes, making it harder to query data efficiently without proper schema design or external indexing solutions.

In Summary, Citus and HBase differ in their data model, partitioning strategies, consistency model, query language support, and secondary indexes availability.

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Advice on HBase, Citus

Feb 9, 2022

Needs adviceon

Milvus

HBase

RocksDB

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!

174k views174k

Comments

Masked

Jun 29, 2021

Needs advice

There'd be a couple of thousands of customers with a similar data structure and a medium number of transactions per day, but the data volume is pretty high (Each customer has around 1 or 2 GB so it would sum up to roughly 2TB). The usage pattern is both read and write-heavy (writes are mostly made through a Windows app, but read operations are made by the user), and I need the historical data for analysis and aggregation. The data model is not join-heavy as is not join-free. If the solution is fully ACID, the better, but must be Highly Available and Horizontally Scalable.

Also, the budget is not so high, and I'd rather be using a handful (at most 5) of cheap to medium-sized servers (2 CPU cores and 4GB RAM).

7.65k views7.65k

Comments

Detailed Comparison

HBase	Citus
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.	It's an extension to Postgres that distributes data and queries in a cluster of multiple machines. Its query engine parallelizes incoming SQL queries across these servers to enable human real-time (less than a second) responses on large datasets.
-	Multi-Node Scalable PostgreSQL;Built-in Replication and High Availability;Real-time Reads/Writes On Multiple Nodes;Multi-core Parallel Processing of Queries;Tenant isolation
Statistics
GitHub Stars 5.5K	GitHub Stars 12.0K
GitHub Forks 3.4K	GitHub Forks 736
Stacks 511	Stacks 60
Followers 498	Followers 124
Votes 15	Votes 11
Pros & Cons
Pros 9 Performance 5 OLTP 1 Fast Point Queries	Pros 6 Multi-core Parallel Processing 3 Drop-in PostgreSQL replacement 2 Distributed with Auto-Sharding
Integrations
No integrations available	.NET Apache Spark Loggly Java Rails Datadog Logentries Heroku Papertrail PostgreSQL

What are some alternatives to HBase, Citus?

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.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

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.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

Related Comparisons

Citus vs HBase: What are the differences?

Data Model: Citus is an extension to PostgreSQL that scales out PostgreSQL across multiple nodes, while HBase is a distributed, non-relational database for large-scale, real-time read/write access to data. Citus utilizes a relational data model, making it easier for users familiar with SQL to work with, while HBase uses a wide-column store data model, which allows for dynamic column creation but requires a different approach to data manipulation compared to SQL databases.
Partitioning: Citus uses sharding to distribute data across multiple nodes based on a shard key, allowing for parallel processing, while HBase uses region servers to divide tables into regions based on row keys, providing automatic load balancing and failover capabilities.
Consistency Model: Citus maintains strong consistency by supporting multi-row transactions across distributed data, ensuring all nodes provide the same data with each request, while HBase offers eventual consistency, where updates are propagated to all nodes eventually, leading to potential inconsistencies for a short period.
Query Language: Citus supports SQL queries due to its PostgreSQL compatibility, making it easier for SQL users to transition, while HBase uses HBase Shell or APIs, such as Java API or REST, for data manipulation, which may require a steeper learning curve for users unfamiliar with these interfaces.
Secondary Indexes: Citus provides support for secondary indexes, allowing faster access to data based on non-primary key attributes, while HBase lacks built-in secondary indexes, making it harder to query data efficiently without proper schema design or external indexing solutions.

In Summary, Citus and HBase differ in their data model, partitioning strategies, consistency model, query language support, and secondary indexes availability.

Citus vs HBase

Overview