Clickhouse vs Elasticsearch

Overview

Elasticsearch

Stacks35.5K

Followers27.1K

Votes1.6K

Clickhouse

Stacks433

Followers543

Votes85

Clickhouse vs Elasticsearch: What are the differences?

Data Model: ClickHouse and Elasticsearch have different data models. ClickHouse uses a columnar data model, which means that data is stored and processed in columns, allowing for efficient compression and faster query execution. On the other hand, Elasticsearch uses a document-based data model, where data is stored and indexed as documents in JSON format. This allows for flexible and schema-less data storage, making it easier to handle unstructured or semi-structured data.
Query Language: ClickHouse and Elasticsearch have different query languages. ClickHouse uses a SQL-like query language, which makes it familiar and easy to use for those who are already familiar with SQL. Elasticsearch, on the other hand, uses its own query DSL (Domain Specific Language) that is specifically designed for full-text search and document retrieval. This means that users need to learn a new query language when working with Elasticsearch.
Scalability: ClickHouse and Elasticsearch have different approaches to scalability. ClickHouse is designed to be horizontally scalable, meaning that it can efficiently handle large amounts of data by adding more servers to a cluster. Elasticsearch, on the other hand, uses a distributed architecture that allows for both horizontally and vertically scalable deployments. This means that Elasticsearch can handle large amounts of data as well as high query loads by scaling both horizontally (adding more nodes) and vertically (adding more resources to a node).
Data Replication and High Availability: ClickHouse and Elasticsearch have different mechanisms to ensure data replication and high availability. ClickHouse uses a synchronous replication model, where data is replicated immediately to multiple replicas to ensure consistency and durability. Elasticsearch, on the other hand, uses an asynchronous replication model, where data is replicated across different nodes in an eventually consistent manner. This means that ClickHouse provides stronger consistency guarantees, while Elasticsearch provides better availability and fault tolerance.
Storage and Indexing: ClickHouse and Elasticsearch have different storage and indexing mechanisms. ClickHouse uses a compressed columnar storage format, which allows for efficient storage and retrieval of data. It also supports indexing on multiple columns to further improve query performance. Elasticsearch uses an inverted index for full-text search, which allows for fast keyword-based search queries on large amounts of text data. Additionally, Elasticsearch supports various analyzers and tokenizers to handle different languages and text formats.
Data Processing and Analytics: ClickHouse and Elasticsearch have different capabilities when it comes to data processing and analytics. ClickHouse is optimized for fast analytical queries, allowing users to perform aggregations, joins, and complex analytical calculations on large data sets. Elasticsearch, on the other hand, is designed for real-time search and analytics, making it suitable for applications that require near real-time indexing and search capabilities. It also provides built-in support for distributed data processing frameworks like Apache Spark and Apache Flink.

In Summary, ClickHouse uses a columnar data model and SQL-like query language for fast analytical queries, while Elasticsearch uses a document-based data model and its own query DSL for flexible document retrieval and full-text search. ClickHouse is horizontally scalable with synchronous replication, while Elasticsearch is both horizontally and vertically scalable with eventual consistency. ClickHouse uses compressed columnar storage and supports indexing, while Elasticsearch uses an inverted index for full-text search. Finally, ClickHouse is optimized for data processing and analytics, while Elasticsearch is designed for real-time search and analytics.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on Elasticsearch, Clickhouse

Rana Usman

Chief Technology Officer at TechAvanza

Jun 4, 2020

Needs adviceon

Firebase

Elasticsearch

Algolia

Hey everybody! (1) I am developing an android application. I have data of around 3 million record (less than a TB). I want to save that data in the cloud. Which company provides the best cloud database services that would suit my scenario? It should be secured, long term useable, and provide better services. I decided to use Firebase Realtime database. Should I stick with Firebase or are there any other companies that provide a better service?

(2) I have the functionality of searching data in my app. Same data (less than a TB). Which search solution should I use in this case? I found Elasticsearch and Algolia search. It should be secure and fast. If any other company provides better services than these, please feel free to suggest them.

Thank you!

408k views408k

Comments

Detailed Comparison

Elasticsearch	Clickhouse
Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).	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.
Distributed and Highly Available Search Engine;Multi Tenant with Multi Types;Various set of APIs including RESTful;Clients available in many languages including Java, Python, .NET, C#, Groovy, and more;Document oriented;Reliable, Asynchronous Write Behind for long term persistency;(Near) Real Time Search;Built on top of Apache Lucene;Per operation consistency;Inverted indices with finite state transducers for full-text querying;BKD trees for storing numeric and geo data;Column store for analytics;Compatible with Hadoop using the ES-Hadoop connector;Open Source under Apache 2 and Elastic License	-
Statistics
Stacks 35.5K	Stacks 433
Followers 27.1K	Followers 543
Votes 1.6K	Votes 85
Pros & Cons
Pros 329 Powerful api 315 Great search engine 231 Open source 214 Restful 200 Near real-time search Cons 7 Resource hungry 6 Diffecult to get started 5 Expensive 4 Hard to keep stable at large scale	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
Kibana Beats Logstash	No integrations available

What are some alternatives to Elasticsearch, 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.

Algolia

Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard.

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.

Related Comparisons

Clickhouse vs Elasticsearch: What are the differences?

Data Model: ClickHouse and Elasticsearch have different data models. ClickHouse uses a columnar data model, which means that data is stored and processed in columns, allowing for efficient compression and faster query execution. On the other hand, Elasticsearch uses a document-based data model, where data is stored and indexed as documents in JSON format. This allows for flexible and schema-less data storage, making it easier to handle unstructured or semi-structured data.
Query Language: ClickHouse and Elasticsearch have different query languages. ClickHouse uses a SQL-like query language, which makes it familiar and easy to use for those who are already familiar with SQL. Elasticsearch, on the other hand, uses its own query DSL (Domain Specific Language) that is specifically designed for full-text search and document retrieval. This means that users need to learn a new query language when working with Elasticsearch.
Scalability: ClickHouse and Elasticsearch have different approaches to scalability. ClickHouse is designed to be horizontally scalable, meaning that it can efficiently handle large amounts of data by adding more servers to a cluster. Elasticsearch, on the other hand, uses a distributed architecture that allows for both horizontally and vertically scalable deployments. This means that Elasticsearch can handle large amounts of data as well as high query loads by scaling both horizontally (adding more nodes) and vertically (adding more resources to a node).
Data Replication and High Availability: ClickHouse and Elasticsearch have different mechanisms to ensure data replication and high availability. ClickHouse uses a synchronous replication model, where data is replicated immediately to multiple replicas to ensure consistency and durability. Elasticsearch, on the other hand, uses an asynchronous replication model, where data is replicated across different nodes in an eventually consistent manner. This means that ClickHouse provides stronger consistency guarantees, while Elasticsearch provides better availability and fault tolerance.
Storage and Indexing: ClickHouse and Elasticsearch have different storage and indexing mechanisms. ClickHouse uses a compressed columnar storage format, which allows for efficient storage and retrieval of data. It also supports indexing on multiple columns to further improve query performance. Elasticsearch uses an inverted index for full-text search, which allows for fast keyword-based search queries on large amounts of text data. Additionally, Elasticsearch supports various analyzers and tokenizers to handle different languages and text formats.
Data Processing and Analytics: ClickHouse and Elasticsearch have different capabilities when it comes to data processing and analytics. ClickHouse is optimized for fast analytical queries, allowing users to perform aggregations, joins, and complex analytical calculations on large data sets. Elasticsearch, on the other hand, is designed for real-time search and analytics, making it suitable for applications that require near real-time indexing and search capabilities. It also provides built-in support for distributed data processing frameworks like Apache Spark and Apache Flink.

Clickhouse vs Elasticsearch

Overview