Cassandra vs OpenTSDB

Overview

Cassandra

Stacks3.6K

Followers3.5K

Votes507

GitHub Stars9.5K

Forks3.8K

OpenTSDB

Stacks32

Followers75

Votes0

GitHub Stars5.1K

Forks1.2K

Cassandra vs OpenTSDB: What are the differences?

Introduction

In this article, we will explore the key differences between Cassandra and OpenTSDB. Both Cassandra and OpenTSDB are popular database systems, but they have some significant differences in terms of architecture, data model, and use cases. Let's dive into the details.

Scalability: Cassandra is a highly scalable distributed database that can handle massive amounts of data across multiple nodes. It follows a masterless architecture and uses a peer-to-peer approach for distributing data. On the other hand, OpenTSDB is built on top of HBase and is not as scalable as Cassandra. It relies on a master-slave architecture, which can potentially become a bottleneck as the data grows.
Data Model: Cassandra is a wide-column store, where data is organized in tables with columns and rows. It provides a flexible schema design and allows for dynamic column addition or removal. OpenTSDB, on the other hand, is a time-series database that stores data points with timestamps. It follows a tag-based model, where each data point is associated with one or more tags. This makes OpenTSDB more suitable for storing and querying time-series data.
Query Language and Performance: Cassandra uses CQL (Cassandra Query Language), which is similar to SQL but with some differences. It supports a wide range of query types, including full-text search, secondary indexes, and aggregate functions. OpenTSDB uses its own query language, which is optimized for time-series data. It offers efficient range queries and supports various aggregation functions specific to time-series analysis. In terms of performance, Cassandra is known for its write-heavy workloads, while OpenTSDB excels in querying large volumes of time-series data.
Consistency and Durability: Cassandra offers tunable consistency levels, allowing users to choose between strong consistency and eventual consistency. It uses a commit log and memtable-based storage to provide durability. OpenTSDB, being built on HBase, provides strong consistency across all writes and uses a write-ahead log for durability. It guarantees that all writes are persisted to disk before being considered successful.
Use Cases: Cassandra is commonly used for real-time data processing, high-volume transactional applications, and distributed data storage and retrieval. It is suitable for use cases that require high availability and scalability, such as IoT applications and social media platforms. OpenTSDB, on the other hand, is specifically designed for time-series data analysis. It is often used in monitoring systems, IoT telemetry, and metrics collection, where the focus is on storing and analyzing time-stamped data.
Ecosystem and Integrations: Cassandra has a large and active community, with a wide range of tools, libraries, and integrations available. It integrates well with popular big data frameworks like Apache Spark and Apache Kafka. OpenTSDB, being built on top of HBase, leverages the Hadoop ecosystem and integrates well with other Hadoop components like Hive and Pig. It also has a plugin system that allows for custom integrations with other systems.

In summary, Cassandra and OpenTSDB differ in scalability, data model, query language and performance, consistency and durability, use cases, and ecosystem and integrations. Depending on the specific requirements of your project, you can choose the database system that best fits your needs.

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Advice on Cassandra, OpenTSDB

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	OpenTSDB
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.	It is a distributed, scalable time series database to store, index & serve metrics collected from computer systems at a large scale. It can store and serve massive amounts of time series data without losing granularity.
-	Store and serve massive amounts of time series data; Scalable
Statistics
GitHub Stars 9.5K	GitHub Stars 5.1K
GitHub Forks 3.8K	GitHub Forks 1.2K
Stacks 3.6K	Stacks 32
Followers 3.5K	Followers 75
Votes 507	Votes 0
Pros & Cons
Pros 119 Distributed 98 High performance 81 High availability 74 Easy scalability 53 Replication Cons 3 Reliability of replication 1 Size 1 Updates	No community feedback yet
Integrations
No integrations available	Grafana HBase

What are some alternatives to Cassandra, OpenTSDB?

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

Cassandra vs OpenTSDB: What are the differences?

Introduction

Scalability: Cassandra is a highly scalable distributed database that can handle massive amounts of data across multiple nodes. It follows a masterless architecture and uses a peer-to-peer approach for distributing data. On the other hand, OpenTSDB is built on top of HBase and is not as scalable as Cassandra. It relies on a master-slave architecture, which can potentially become a bottleneck as the data grows.
Data Model: Cassandra is a wide-column store, where data is organized in tables with columns and rows. It provides a flexible schema design and allows for dynamic column addition or removal. OpenTSDB, on the other hand, is a time-series database that stores data points with timestamps. It follows a tag-based model, where each data point is associated with one or more tags. This makes OpenTSDB more suitable for storing and querying time-series data.
Query Language and Performance: Cassandra uses CQL (Cassandra Query Language), which is similar to SQL but with some differences. It supports a wide range of query types, including full-text search, secondary indexes, and aggregate functions. OpenTSDB uses its own query language, which is optimized for time-series data. It offers efficient range queries and supports various aggregation functions specific to time-series analysis. In terms of performance, Cassandra is known for its write-heavy workloads, while OpenTSDB excels in querying large volumes of time-series data.
Consistency and Durability: Cassandra offers tunable consistency levels, allowing users to choose between strong consistency and eventual consistency. It uses a commit log and memtable-based storage to provide durability. OpenTSDB, being built on HBase, provides strong consistency across all writes and uses a write-ahead log for durability. It guarantees that all writes are persisted to disk before being considered successful.
Use Cases: Cassandra is commonly used for real-time data processing, high-volume transactional applications, and distributed data storage and retrieval. It is suitable for use cases that require high availability and scalability, such as IoT applications and social media platforms. OpenTSDB, on the other hand, is specifically designed for time-series data analysis. It is often used in monitoring systems, IoT telemetry, and metrics collection, where the focus is on storing and analyzing time-stamped data.
Ecosystem and Integrations: Cassandra has a large and active community, with a wide range of tools, libraries, and integrations available. It integrates well with popular big data frameworks like Apache Spark and Apache Kafka. OpenTSDB, being built on top of HBase, leverages the Hadoop ecosystem and integrates well with other Hadoop components like Hive and Pig. It also has a plugin system that allows for custom integrations with other systems.

Cassandra vs OpenTSDB

Overview