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  1. Stackups
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  3. Databases
  4. Databases
  5. LevelDB vs RocksDB

LevelDB vs RocksDB

OverviewComparisonAlternatives

Overview

LevelDB
LevelDB
Stacks108
Followers111
Votes0
GitHub Stars38.3K
Forks8.1K
RocksDB
RocksDB
Stacks141
Followers290
Votes11
GitHub Stars30.9K
Forks6.6K

LevelDB vs RocksDB: What are the differences?

LevelDB and RocksDB are both open-source, embedded key-value storage engines, offering high performance and scalability for various applications. Let's explore the key differences between them.

  1. Storage Format: LevelDB stores data in a sorted key-value format, using a log-structured merge-tree (LSM-tree) for efficient storage and retrieval. On the other hand, RocksDB also uses an LSM-tree structure, but it provides support for various alternative storage engines and formats, such as plain table format, block-based table format, and Cuckoo Hashmap format, offering more flexibility in storage options.

  2. Performance Optimization: LevelDB is optimized for read-heavy workloads and can efficiently handle random read operations. In contrast, RocksDB is designed to handle both read and write-heavy workloads, providing better performance for write operations, as it implements certain optimizations like write-ahead logs and block-based filters.

  3. Concurrency Control: While LevelDB supports concurrency through a single writer thread and multiple reader threads, RocksDB takes concurrency to the next level by allowing multiple writer threads along with multiple reader threads. This makes RocksDB better suited for scenarios with high write concurrency.

  4. Write Amplification: LevelDB can suffer from higher write amplification, which means it writes more data to disk than what is being written by the application. In RocksDB, write amplification is reduced by implementing a number of techniques like memtable compression and block-based table format, leading to better disk utilization and reduced write amplification.

  5. Integration with other Databases: RocksDB offers better integration with other databases and storage systems. It provides APIs and tools to easily integrate with systems like Apache Hadoop, MongoDB, and Cassandra, facilitating data management and transfer across different environments. LevelDB, on the other hand, is more standalone and not as extensively integrated with other databases.

  6. Memory Management: RocksDB allows fine-grained control over memory management through the use of various configurable options, such as block cache, write buffer size, and index and filter block size. This enables users to optimize memory usage based on their specific requirements. LevelDB has more limited memory management capabilities compared to RocksDB.

In summary, LevelDB provides a simple key-value storage engine with basic features suitable for lightweight applications. RocksDB, built on top of LevelDB, offers enhanced performance, reliability, and advanced features such as multi-threaded execution, compaction optimizations, and support for larger datasets, making it more suitable for high-throughput and low-latency use cases.

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Detailed Comparison

LevelDB
LevelDB
RocksDB
RocksDB

It is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values. It has been ported to a variety of Unix-based systems, macOS, Windows, and Android.

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.

Simple key-value stores with Go, C++, Node.js and more!
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
38.3K
GitHub Stars
30.9K
GitHub Forks
8.1K
GitHub Forks
6.6K
Stacks
108
Stacks
141
Followers
111
Followers
290
Votes
0
Votes
11
Pros & Cons
No community feedback yet
Pros
  • 5
    Very fast
  • 3
    Made by Facebook
  • 2
    Consistent performance
  • 1
    Ability to add logic to the database layer where needed
Integrations
Java
Java
Windows
Windows
macOS
macOS
No integrations available

What are some alternatives to LevelDB, 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.

Memcached

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

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.

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