Clickhouse vs Sybase

Overview

Clickhouse

Stacks431

Followers543

Votes85

Sybase

Stacks41

Followers80

Votes10

Clickhouse vs Sybase: What are the differences?

# Introduction

1. **Data Model**: Clickhouse is column-oriented, while Sybase is row-oriented. Clickhouse stores data in columns, which allows for efficient data compression, high query performance, and analytical capabilities. On the other hand, Sybase stores data in rows, making it suitable for transactional processing.
   
2. **Partitioning**: Clickhouse supports automatic partitioning based on the value of a specific column, which helps in query performance and data management. Sybase also supports partitioning but requires manual intervention and setup.
   
3. **Indexing**: Clickhouse relies heavily on its unique index structure called MergeTree, which enables efficient data retrieval. Sybase utilizes traditional B-tree indexes for data retrieval operations.
   
4. **Replication**: Clickhouse supports asynchronous data replication, making it suitable for scenarios where high availability and fault tolerance are crucial. Sybase also supports replication but may require additional configurations and expertise.
   
5. **Query Language**: Clickhouse uses a SQL-like language specifically optimized for analytical queries, while Sybase uses Transact-SQL, which is more suitable for transactional operations.
   
6. **Scalability**: Clickhouse is designed for horizontal scalability and performs exceptionally well when dealing with large datasets and concurrent queries. Sybase also supports scalability but may require additional resources and configurations to achieve optimal performance.

In Summary, Clickhouse and Sybase differ in their data models, partitioning methods, indexing structures, replication capabilities, query languages, and scalability options.

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Detailed Comparison

Clickhouse	Sybase
It allows analysis of data that is updated in real time. It offers instant results in most cases: the data is processed faster than it takes to create a query.	Modernize and accelerate your transaction-based applications on premise and in the cloud. This high-performance SQL database server uses a relational management model to meet rising demand for performance, reliability, and efficiency in every industry.
-	Faster, more secure transfer of database files; Multiversion concurrency control (MVCC); Three-system monitoring procedures
Statistics
Stacks 431	Stacks 41
Followers 543	Followers 80
Votes 85	Votes 10
Pros & Cons
Pros 21 Fast, very very fast 11 Good compression ratio 7 Horizontally scalable 6 Utilizes all CPU resources 5 Open-source Cons 5 Slow insert operations	Pros 1 Replication server the best 1 SAP Replication server este net superior replicarii din 1 Sybase has at least 200000 from 15 years ago 1 Very good for application with high number of connectio 1 HADR dont loose data

What are some alternatives to Clickhouse, Sybase?

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.

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 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.

Related Comparisons

Clickhouse vs Sybase: What are the differences?

# Introduction

1. **Data Model**: Clickhouse is column-oriented, while Sybase is row-oriented. Clickhouse stores data in columns, which allows for efficient data compression, high query performance, and analytical capabilities. On the other hand, Sybase stores data in rows, making it suitable for transactional processing.
   
2. **Partitioning**: Clickhouse supports automatic partitioning based on the value of a specific column, which helps in query performance and data management. Sybase also supports partitioning but requires manual intervention and setup.
   
3. **Indexing**: Clickhouse relies heavily on its unique index structure called MergeTree, which enables efficient data retrieval. Sybase utilizes traditional B-tree indexes for data retrieval operations.
   
4. **Replication**: Clickhouse supports asynchronous data replication, making it suitable for scenarios where high availability and fault tolerance are crucial. Sybase also supports replication but may require additional configurations and expertise.
   
5. **Query Language**: Clickhouse uses a SQL-like language specifically optimized for analytical queries, while Sybase uses Transact-SQL, which is more suitable for transactional operations.
   
6. **Scalability**: Clickhouse is designed for horizontal scalability and performs exceptionally well when dealing with large datasets and concurrent queries. Sybase also supports scalability but may require additional resources and configurations to achieve optimal performance.

In Summary, Clickhouse and Sybase differ in their data models, partitioning methods, indexing structures, replication capabilities, query languages, and scalability options.

Clickhouse vs Sybase

Overview