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  1. Stackups
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  3. Databases
  4. Big Data Tools
  5. Apache Spark vs TiDB

Apache Spark vs TiDB

OverviewDecisionsComparisonAlternatives

Overview

Apache Spark
Apache Spark
Stacks3.1K
Followers3.5K
Votes140
GitHub Stars42.2K
Forks28.9K
TiDB
TiDB
Stacks76
Followers177
Votes28
GitHub Stars39.3K
Forks6.0K

Apache Spark vs TiDB: What are the differences?

What is Apache Spark? Fast and general engine for large-scale data processing. Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

What is TiDB? A distributed NewSQL database compatible with MySQL protocol. Inspired by the design of Google F1, TiDB supports the best features of both traditional RDBMS and NoSQL.

Apache Spark and TiDB are primarily classified as "Big Data" and "Databases" tools respectively.

Some of the features offered by Apache Spark are:

  • Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk
  • Write applications quickly in Java, Scala or Python
  • Combine SQL, streaming, and complex analytics

On the other hand, TiDB provides the following key features:

  • Horizontal scalability
  • Asynchronous schema changes
  • Consistent distributed transactions

Apache Spark and TiDB are both open source tools. Apache Spark with 22.5K GitHub stars and 19.4K forks on GitHub appears to be more popular than TiDB with 19.6K GitHub stars and 2.86K GitHub forks.

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Advice on Apache Spark, TiDB

Nilesh
Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceonElasticsearchElasticsearchKafkaKafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

576k views576k
Comments

Detailed Comparison

Apache Spark
Apache Spark
TiDB
TiDB

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

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

Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3
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
42.2K
GitHub Stars
39.3K
GitHub Forks
28.9K
GitHub Forks
6.0K
Stacks
3.1K
Stacks
76
Followers
3.5K
Followers
177
Votes
140
Votes
28
Pros & Cons
Pros
  • 61
    Open-source
  • 48
    Fast and Flexible
  • 8
    Great for distributed SQL like applications
  • 8
    One platform for every big data problem
  • 6
    Easy to install and to use
Cons
  • 4
    Speed
Pros
  • 9
    Open source
  • 7
    Horizontal scalability
  • 5
    Strong ACID
  • 3
    HTAP
  • 2
    Mysql Compatibility

What are some alternatives to Apache Spark, 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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