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  1. Stackups
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  4. Databases
  5. Memcached vs RocksDB

Memcached vs RocksDB

OverviewDecisionsComparisonAlternatives

Overview

Memcached
Memcached
Stacks7.9K
Followers5.7K
Votes473
GitHub Stars14.0K
Forks3.3K
RocksDB
RocksDB
Stacks141
Followers290
Votes11
GitHub Stars30.9K
Forks6.6K

Memcached vs RocksDB: What are the differences?

Key differences between Memcached and RocksDB

Memcached and RocksDB are both popular in-memory data storage systems used in web applications. However, they have several key differences that make them suitable for different purposes.

  1. Implementation and data storage:

    • Memcached is an in-memory key-value store that stores data in a hashtable in RAM. It is simple and lightweight, designed for fast data access and caching.
    • RocksDB, on the other hand, is a persistent embedded key-value store that stores data on disk. It is optimized for durable storage and can handle large datasets.
  2. Durability and persistence:

    • Memcached does not provide durability or persistence out of the box. If the system crashes or restarts, all the cached data is lost. It is typically used for caching transient and non-critical data.
    • RocksDB, being a disk-based storage system, provides durability and persistence. It flushes data to disk periodically and can recover data in case of system failures.
  3. Performance and scalability:

    • Memcached is known for its exceptional performance in terms of storing and retrieving data from memory quickly. It is highly scalable and can handle a large number of concurrent requests.
    • RocksDB, while not as fast as Memcached for in-memory operations, offers better performance for disk-based storage. It can handle large datasets efficiently and is designed for high read and write throughput.
  4. Data consistency and atomicity:

    • Memcached does not provide built-in support for data consistency or atomic operations. It is eventually consistent and relies on the application layer for maintaining data integrity.
    • RocksDB supports atomic and consistent operations on a single key-value pair. It ensures that read and write operations on a single key are ACID-compliant.
  5. Data size and storage capacity:

    • Memcached is limited by the amount of memory available in the system. It is not suitable for storing large datasets that exceed the available memory.
    • RocksDB can handle large datasets that can exceed the available RAM. It efficiently manages data on disk and allows applications to handle larger workloads.
  6. Usability and ease of deployment:

    • Memcached is extremely easy to deploy and configure. It has a simple and straightforward API, making it suitable for lightweight caching and session storage use cases.
    • RocksDB requires more setup and configuration as it needs to be integrated into the application. It provides a more granular level of control and flexibility for data storage and retrieval.

In summary, Memcached is a lightweight, in-memory caching system optimized for performance and scalability, while RocksDB is a durable disk-based storage system designed for managing large datasets efficiently.

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Advice on Memcached, RocksDB

D
D

Feb 9, 2022

Needs adviceonMilvusMilvusHBaseHBaseRocksDBRocksDB

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

Detailed Comparison

Memcached
Memcached
RocksDB
RocksDB

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.

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.

-
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
Statistics
GitHub Stars
14.0K
GitHub Stars
30.9K
GitHub Forks
3.3K
GitHub Forks
6.6K
Stacks
7.9K
Stacks
141
Followers
5.7K
Followers
290
Votes
473
Votes
11
Pros & Cons
Pros
  • 139
    Fast object cache
  • 129
    High-performance
  • 91
    Stable
  • 65
    Mature
  • 33
    Distributed caching system
Cons
  • 2
    Only caches simple types
Pros
  • 5
    Very fast
  • 3
    Made by Facebook
  • 2
    Consistent performance
  • 1
    Ability to add logic to the database layer where needed

What are some alternatives to Memcached, RocksDB?

MongoDB

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

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

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

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

SQLite

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

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.

MariaDB

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

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

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.

InfluxDB

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

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