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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data Tools
  5. Druid vs TiDB

Druid vs TiDB

OverviewComparisonAlternatives

Overview

Druid
Druid
Stacks377
Followers867
Votes32
TiDB
TiDB
Stacks76
Followers177
Votes28
GitHub Stars39.3K
Forks6.0K

Druid vs TiDB: What are the differences?

# Introduction

Key differences between Druid and TiDB:

1. **Data Model**: Druid is a time series database that stores data in immutable segments, while TiDB is a distributed SQL database that uses a distributed key-value model.
   
2. **Consistency**: Druid emphasizes eventual consistency for real-time analytics workloads, whereas TiDB ensures strong consistency with the ACID properties for OLTP scenarios.
   
3. **Query Language**: Druid uses its SQL-like query language called Druid Query Language (DQL), while TiDB supports traditional SQL queries with standard SQL compatibility.
  
4. **Scalability**: Druid is designed for scalability with horizontal data partitioning and distributed architecture, whereas TiDB offers horizontal scalability and can seamlessly scale out by adding more nodes.

5. **Storage Engine**: Druid uses a column-oriented storage engine optimized for time-series data, while TiDB combines a row store and a columnar store for both OLTP and OLAP workloads.

6. **Consolidation vs. Separation**: Druid separates storage and processing layers for better performance and flexibility, while TiDB combines storage and processing in a single layer for simplicity and ease of deployment.

# Summary

In Summary, the key differences between Druid and TiDB encompass their underlying data models, consistency levels, query languages, scalability approaches, storage engines, and architectural design for handling data workloads.

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Detailed Comparison

Druid
Druid
TiDB
TiDB

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Inspired by the design of Google F1, TiDB supports the best features of both traditional RDBMS and NoSQL.

-
Horizontal scalability;Asynchronous schema changes;Consistent distributed transactions;Compatible with MySQL protocol;Written in Go;NewSQL over TiKV;Multiple storage engine support
Statistics
GitHub Stars
-
GitHub Stars
39.3K
GitHub Forks
-
GitHub Forks
6.0K
Stacks
377
Stacks
76
Followers
867
Followers
177
Votes
32
Votes
28
Pros & Cons
Pros
  • 15
    Real Time Aggregations
  • 6
    Batch and Real-Time Ingestion
  • 5
    OLAP
  • 3
    OLAP + OLTP
  • 2
    Combining stream and historical analytics
Cons
  • 3
    Limited sql support
  • 2
    Joins are not supported well
  • 1
    Complexity
Pros
  • 9
    Open source
  • 7
    Horizontal scalability
  • 5
    Strong ACID
  • 3
    HTAP
  • 2
    Enterprise Support
Integrations
Zookeeper
Zookeeper
No integrations available

What are some alternatives to Druid, TiDB?

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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