Cassandra vs Event Store

Overview

Cassandra

Stacks3.6K

Followers3.5K

Votes507

GitHub Stars9.5K

Forks3.8K

Event Store

Stacks69

Followers82

Votes1

Cassandra vs Event Store: What are the differences?

Introduction Cassandra and Event Store are both popular database technologies. While they share certain similarities, there are key differences that set them apart. This markdown code provides a clear comparison, highlighting the unique features and functionalities of each database.

1. Architecture and Data Model: Cassandra follows a distributed peer-to-peer architecture, with a masterless design and a flexible column-based data model. It is highly scalable and fault-tolerant, allowing for horizontal expansion. On the other hand, Event Store follows an event sourcing architecture, where data is stored as a series of events. It is an append-only log of events, enabling easy auditability and temporal querying.

2. Event Marshalling and Indexing: In Cassandra, data is serialized into key-value pairs and stored in a sorted order using a hash-based index structure. This allows for fast read and write operations. In contrast, Event Store stores events in their raw form without any predefined schema. It uses event types, event IDs, and event streams for indexing and retrieval, supporting quick access to events based on time and stream.

3. Querying Capabilities: Cassandra supports a rich set of query options, including CQL (Cassandra Query Language), which resembles SQL. It allows for ad-hoc queries, secondary indexes, and supports range, equality, and token-based partition queries. Event Store provides various ways to query events, such as by event type, stream ID, or time. It supports event position-based and stream-based querying, enabling historical and real-time event retrieval.

4. Consistency and Durability: Cassandra offers tunable consistency levels, allowing developers to balance between strong consistency and high availability. It provides eventual consistency by default, ensuring durability through write-ahead logging and mem-table to SSTable persistence. Event Store guarantees strong consistency, as events are written atomically within a stream. It stores events in multiple replicas for fault-tolerance and durability.

5. Fault Tolerance and Replication: Cassandra provides tunable replication, allowing for transparent data distribution across multiple nodes. It employs peer-to-peer gossip-based protocols for failure detection and automatic partitioning. Event Store uses the concept of clusters and nodes to ensure fault tolerance and replication. It replicates events across groups of nodes, forming a cluster with built-in leader election and automatic failover.

6. Use Cases and Application Scenarios: Cassandra is commonly used for high-volume and low-latency applications, such as real-time analytics, messaging platforms, and content management systems. It excels in write-heavy workloads and use cases requiring massive scale. Event Store is specifically designed for event-driven architectures, event sourcing, and event-driven microservices. It is ideal for scenarios like auditing, event sourcing, and financial systems.

In summary, Cassandra's distributed architecture, flexible data model, and rich querying capabilities make it suitable for large-scale applications with high write throughput. On the other hand, Event Store's event sourcing model, strong consistency, and focus on event-driven architectures position it as a specialized database for event-driven scenarios requiring temporal querying and auditability.

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Advice on Cassandra, Event Store

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.

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Detailed Comparison

Cassandra	Event Store
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 stores your data as a series of immutable events over time, making it easy to build event-sourced applications. It can run as a cluster of nodes containing the same data, which remains available for writes provided at least half the nodes are alive and connected.
-	Guaranteed writes; High availability; Projections; Multiple client interfaces; Optimistic concurrency checks; Subscribe to streams with competing consumers; Great performance that scales; Multiple hosting options; Commercial support plans; Immutable data store; Atom subscriptions
Statistics
GitHub Stars 9.5K	GitHub Stars -
GitHub Forks 3.8K	GitHub Forks -
Stacks 3.6K	Stacks 69
Followers 3.5K	Followers 82
Votes 507	Votes 1
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	Pros 1 Trail Log
Integrations
No integrations available	.NET SQLite MySQL

What are some alternatives to Cassandra, Event Store?

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 Event Store: What are the differences?

Cassandra vs Event Store

Overview