CrateIO vs RocksDB

Overview

RocksDB

Stacks141

Followers290

Votes11

GitHub Stars30.9K

Forks6.6K

CrateIO

Stacks19

Followers39

Votes7

GitHub Stars4.3K

Forks581

CrateIO vs RocksDB: What are the differences?

Storage Engine: CrateIO utilizes a distributed SQL database whereas RocksDB is an embeddable persistent key-value store for fast storage.
Data Model: CrateIO offers a SQL interface for data manipulation while RocksDB operates with a simple key-value data model.
Scalability: CrateIO is designed for horizontal scalability and data distribution across multiple nodes, while RocksDB is more suitable for single-node deployments.
Query Language: CrateIO supports SQL queries for fetching and manipulating data, whereas RocksDB does not have a built-in query language.
Community Support: CrateIO benefits from an active community and commercial support, whereas RocksDB has more limited community-driven development and support.
Use Cases: CrateIO is commonly used for large-scale distributed data processing and analytics, while RocksDB is often used in applications that require high-performance local key-value storage.

In Summary, CrateIO and RocksDB differ in their storage engines, data models, scalability, query languages, community support, and typical use cases.

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Advice on RocksDB, CrateIO

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!

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Detailed Comparison

RocksDB	CrateIO
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.	Crate is a distributed data store. Simply install Crate directly on your application servers and make the big centralized database a thing of the past. Crate takes care of synchronization, sharding, scaling, and replication even for mammoth data sets.
Designed for application servers wanting to store up to a few terabytes of data on locally attached Flash drives or in RAM;Optimized for storing small to medium size key-values on fast storage -- flash devices or in-memory;Scales linearly with number of CPUs so that it works well on ARM processors	Familiar SQL syntax;Semi-structured data;High availability, resiliency, and scalability in a distributed design;Powerful Lucene based full-text search
Statistics
GitHub Stars 30.9K	GitHub Stars 4.3K
GitHub Forks 6.6K	GitHub Forks 581
Stacks 141	Stacks 19
Followers 290	Followers 39
Votes 11	Votes 7
Pros & Cons
Pros 5 Very fast 3 Made by Facebook 2 Consistent performance 1 Ability to add logic to the database layer where needed	Pros 3 Simplicity 2 Scale 2 Open source
Integrations
No integrations available	Docker

What are some alternatives to RocksDB, CrateIO?

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

CrateIO vs RocksDB: What are the differences?

Storage Engine: CrateIO utilizes a distributed SQL database whereas RocksDB is an embeddable persistent key-value store for fast storage.
Data Model: CrateIO offers a SQL interface for data manipulation while RocksDB operates with a simple key-value data model.
Scalability: CrateIO is designed for horizontal scalability and data distribution across multiple nodes, while RocksDB is more suitable for single-node deployments.
Query Language: CrateIO supports SQL queries for fetching and manipulating data, whereas RocksDB does not have a built-in query language.
Community Support: CrateIO benefits from an active community and commercial support, whereas RocksDB has more limited community-driven development and support.
Use Cases: CrateIO is commonly used for large-scale distributed data processing and analytics, while RocksDB is often used in applications that require high-performance local key-value storage.

In Summary, CrateIO and RocksDB differ in their storage engines, data models, scalability, query languages, community support, and typical use cases.

CrateIO vs RocksDB

Overview