Apache Kudu vs Cassandra

Overview

Cassandra

Stacks3.6K

Followers3.5K

Votes507

GitHub Stars9.5K

Forks3.8K

Apache Kudu

Stacks71

Followers259

Votes10

GitHub Stars828

Forks282

Apache Kudu vs Cassandra: What are the differences?

Apache Kudu vs. Cassandra

Apache Kudu and Cassandra are both popular distributed database management systems used for handling big data. However, there are several notable differences between the two.

1. Storage Architecture: Apache Kudu utilizes a columnar storage architecture, which provides fast analytical scans and aggregation queries. On the other hand, Cassandra uses a row-based storage architecture, making it better suited for high write throughput and transactional workloads.

2. Data Model: Cassandra follows a wide-column store data model, where data is organized into rows with multiple columns. It supports flexible schema design and allows for the storage of large amounts of structured and semi-structured data. In contrast, Kudu adopts a table-like, structured data model with strong schema enforcement, making it more appropriate for use cases that require strict data consistency.

3. Consistency Model: Cassandra employs an eventual consistency model, allowing data to be written to multiple replicas with a possibility of data inconsistencies that are resolved over time. On the contrary, Kudu offers strong consistency guarantees, ensuring that all read operations are always consistent with the most recent write.

4. Secondary Indexes: Kudu provides native support for secondary indexes, allowing efficient search operations on multiple columns. On the other hand, Cassandra requires the use of external tools or custom solutions for secondary indexing.

5. Data Compression and Compression: Kudu supports efficient data compression algorithms, enabling reduced storage requirements and improved query performance. Additionally, it provides support for automatic data compaction, which ensures optimal disk space utilization. In contrast, Cassandra does not offer built-in data compression or automatic compaction capabilities.

6. Query Language Support: Cassandra uses its proprietary query language, Cassandra Query Language (CQL), which is similar to SQL but with some differences. Kudu, on the other hand, provides an extensive SQL-like query language, making it easier for users familiar with SQL to work with the database.

In summary, Apache Kudu and Cassandra differ in their storage architecture, data model, consistency model, support for secondary indexes, data compression and compaction, and query language support. These distinctions make each database system suitable for specific use cases and scenarios.

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Advice on Cassandra, Apache Kudu

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	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

What are some alternatives to Cassandra, Apache Kudu?

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.

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.

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.

Related Comparisons

Apache Kudu vs Cassandra: What are the differences?

Apache Kudu vs. Cassandra

Apache Kudu and Cassandra are both popular distributed database management systems used for handling big data. However, there are several notable differences between the two.

Apache Kudu vs Cassandra

Overview

Apache Kudu vs Cassandra: What are the differences?