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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. Cassandra vs Kudu

Cassandra vs Kudu

OverviewDecisionsComparisonAlternatives

Overview

Cassandra
Cassandra
Stacks3.6K
Followers3.5K
Votes507
GitHub Stars9.5K
Forks3.8K
Apache Kudu
Apache Kudu
Stacks71
Followers259
Votes10
GitHub Stars828
Forks282

Cassandra vs Kudu: What are the differences?

## Key Differences between Cassandra and Kudu

Cassandra is a distributed NoSQL database that is designed to handle large amounts of data across multiple servers. It is optimized for write-heavy workloads and provides high availability and scalability. On the other hand, Kudu is a columnar storage engine developed by Cloudera that is integrated with Apache Hadoop. It is designed to efficiently store and process data for analytics workloads, providing fast scans and aggregations.

1. **Data Model**:
Cassandra uses a wide-column store data model that is based on a key-value structure. It provides flexibility in data representation with its schema-free approach. In contrast, Kudu uses a columnar data model that organizes data in columns rather than rows, allowing for efficient data compression and query performance.

2. **Secondary Indexes**:
Cassandra supports secondary indexes for querying data based on non-primary key columns, but they come with performance trade-offs and limitations. Kudu, on the other hand, supports automatic and manual secondary indexes, which can improve query performance by allowing efficient filtering and sorting.

3. **Consistency Model**:
Cassandra offers tunable consistency levels, allowing users to choose between strong consistency and eventual consistency based on their application requirements. Kudu provides strong consistency guarantees by default, ensuring that reads always reflect the latest data written.

4. **Compression**:
Cassandra supports data compression to reduce storage space and improve read performance. It uses techniques like LZ4 and Snappy compression. Kudu also offers data compression for columnar data, utilizing algorithms like Zstandard and LZO to optimize storage and query speed.

5. **Data Integrity**:
Cassandra ensures data integrity through features like tunable consistency, replication, and built-in repair mechanisms. Kudu provides data integrity through atomic and consistent operations, ensuring that data remains accurate and reliable during read and write operations.

6. **Query Performance**:
Cassandra is optimized for fast writes and can handle high throughput for write-heavy workloads. Kudu excels in query performance for analytical workloads, providing efficient scans, aggregations, and analytical queries on large datasets.

In Summary, Cassandra and Kudu differ in their data models, secondary index support, consistency models, compression techniques, data integrity mechanisms, and query performance optimizations.

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Advice on Cassandra, Apache Kudu

Vinay
Vinay

Head of Engineering

Sep 19, 2019

Needs advice

The problem I have is - we need to process & change(update/insert) 55M Data every 2 min and this updated data to be available for Rest API for Filtering / Selection. Response time for Rest API should be less than 1 sec.

The most important factors for me are processing and storing time of 2 min. There need to be 2 views of Data One is for Selection & 2. Changed data.

174k views174k
Comments

Detailed Comparison

Cassandra
Cassandra
Apache Kudu
Apache Kudu

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.

A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data.

Statistics
GitHub Stars
9.5K
GitHub Stars
828
GitHub Forks
3.8K
GitHub Forks
282
Stacks
3.6K
Stacks
71
Followers
3.5K
Followers
259
Votes
507
Votes
10
Pros & Cons
Pros
  • 119
    Distributed
  • 98
    High performance
  • 81
    High availability
  • 74
    Easy scalability
  • 53
    Replication
Cons
  • 3
    Reliability of replication
  • 1
    Updates
  • 1
    Size
Pros
  • 10
    Realtime Analytics
Cons
  • 1
    Restart time
Integrations
No integrations available
Hadoop
Hadoop

What are some alternatives to Cassandra, Apache Kudu?

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.

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.

InfluxDB

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

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