Clickhouse vs LevelDB

Overview

LevelDB

Stacks108

Followers111

Votes0

GitHub Stars38.3K

Forks8.1K

Clickhouse

Stacks431

Followers543

Votes85

Clickhouse vs LevelDB: What are the differences?

Introduction

ClickHouse and LevelDB are both popular database systems used for data storage and processing. Despite both being designed for high-performance operations, they have key differences that make them suitable for different use cases.

Data Model and Query Language: ClickHouse uses a SQL-like query language for querying data, making it easier for users familiar with SQL to interact with the database. On the other hand, LevelDB does not have a built-in query language, and data retrieval is typically done programmatically using key-value pairs.
Data Storage: ClickHouse is optimized for analytical workloads with a column-oriented storage model that allows for efficient querying and aggregation of large datasets. In contrast, LevelDB is a key-value store that is better suited for simple read/write operations on smaller datasets.
Concurrency and Performance: ClickHouse is designed for handling high-concurrency analytical workloads efficiently, with support for executing multiple complex queries in parallel. LevelDB, while efficient for single-threaded read/write operations, may not perform as well in high-concurrency environments.
Use Cases: ClickHouse is commonly used in data warehousing and analytics applications where fast querying and processing of large datasets are essential. LevelDB, on the other hand, is frequently used in embedded systems, caching layers, and applications that require fast key-value lookups.
Community and Ecosystem: ClickHouse has a growing community and ecosystem with support for integrations with various data sources and visualization tools. LevelDB, while widely used for its simplicity and performance in key-value storage, may have a smaller ecosystem compared to ClickHouse.
Scalability and Fault Tolerance: ClickHouse is designed to be horizontally scalable and provides built-in features for replication and fault tolerance. In contrast, LevelDB does not offer native support for distributed operations and may require custom implementations for achieving scalability and fault tolerance.

In Summary, ClickHouse and LevelDB have key differences in their data model, query language, storage design, use cases, community support, and scalability, making them better suited for different types of applications.

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CLI (Node.js)

Manual

Detailed Comparison

LevelDB	Clickhouse
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.	It allows analysis of data that is updated in real time. It offers instant results in most cases: the data is processed faster than it takes to create a query.
Simple key-value stores with Go, C++, Node.js and more!	-
Statistics
GitHub Stars 38.3K	GitHub Stars -
GitHub Forks 8.1K	GitHub Forks -
Stacks 108	Stacks 431
Followers 111	Followers 543
Votes 0	Votes 85
Pros & Cons
No community feedback yet	Pros 21 Fast, very very fast 11 Good compression ratio 7 Horizontally scalable 6 Utilizes all CPU resources 5 Great CLI Cons 5 Slow insert operations
Integrations
Java Windows macOS	No integrations available

What are some alternatives to LevelDB, Clickhouse?

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

Clickhouse vs LevelDB: What are the differences?

Introduction

Data Model and Query Language: ClickHouse uses a SQL-like query language for querying data, making it easier for users familiar with SQL to interact with the database. On the other hand, LevelDB does not have a built-in query language, and data retrieval is typically done programmatically using key-value pairs.
Data Storage: ClickHouse is optimized for analytical workloads with a column-oriented storage model that allows for efficient querying and aggregation of large datasets. In contrast, LevelDB is a key-value store that is better suited for simple read/write operations on smaller datasets.
Concurrency and Performance: ClickHouse is designed for handling high-concurrency analytical workloads efficiently, with support for executing multiple complex queries in parallel. LevelDB, while efficient for single-threaded read/write operations, may not perform as well in high-concurrency environments.
Use Cases: ClickHouse is commonly used in data warehousing and analytics applications where fast querying and processing of large datasets are essential. LevelDB, on the other hand, is frequently used in embedded systems, caching layers, and applications that require fast key-value lookups.
Community and Ecosystem: ClickHouse has a growing community and ecosystem with support for integrations with various data sources and visualization tools. LevelDB, while widely used for its simplicity and performance in key-value storage, may have a smaller ecosystem compared to ClickHouse.
Scalability and Fault Tolerance: ClickHouse is designed to be horizontally scalable and provides built-in features for replication and fault tolerance. In contrast, LevelDB does not offer native support for distributed operations and may require custom implementations for achieving scalability and fault tolerance.

Clickhouse vs LevelDB

Overview

Clickhouse vs LevelDB: What are the differences?