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=== What is memcp? ===
=== What is memcp? ===
[[File:Webapps.svg|left|frameless]]
memcp is an open-source, high-performance, columnar in-memory database that can handle both OLAP and OLTP workloads. It provides an alternative to proprietary analytical databases and aims to bring the benefits of columnar storage to the open-source world.
memcp is an open-source, high-performance, columnar in-memory database that can handle both OLAP and OLTP workloads. It provides an alternative to proprietary analytical databases and aims to bring the benefits of columnar storage to the open-source world.


memcp is written in Golang and is designed to be portable and extensible, allowing developers to embed the database into their applications with ease. It is also designed with a focus on scalability and performance, making it a suitable choice for distributed applications.
memcp is written in Golang and is designed to be portable and extensible, allowing developers to embed the database into their applications with ease. It is also designed with a focus on scalability and performance, making it a suitable choice for distributed applications.


=== Features ===


* '''fast:''' MemCP is built with parallelization in mind. The parallelization pattern is made for minimal overhead.
===Features===
* '''efficient:''' The average compression ratio is 1:5 (80% memory saving) compared to MySQL/MariaDB
 
* '''modern:''' MemCP is built for modern hardware with caches, NUMA memory, multicore CPUs, NVMe SSDs
*'''fast:''' MemCP is built with parallelization in mind. The parallelization pattern is made for minimal overhead.
*'''efficient:''' The average compression ratio is 1:5 (80% memory saving) compared to MySQL/MariaDB
*'''modern:''' MemCP is built for modern hardware with caches, NUMA memory, multicore CPUs, NVMe SSDs
* '''versatile:''' Use it in big mainframes to gain analytical performance, use it in embedded systems to conserve flash lifetime
* '''versatile:''' Use it in big mainframes to gain analytical performance, use it in embedded systems to conserve flash lifetime
* Columnar storage: Stores data column-wise instead of row-wise, which allows for better compression, faster query execution, and more efficient use of memory.
* Columnar storage: Stores data column-wise instead of row-wise, which allows for better compression, faster query execution, and more efficient use of memory.
* In-memory database: Stores all data in memory, which allows for extremely fast query execution.
* In-memory database: Stores all data in memory, which allows for extremely fast query execution.
* Build fast REST APIs directly in the database (they are faster because there is no network connection / SQL layer in between)
*Build fast REST APIs directly in the database (they are faster because there is no network connection / SQL layer in between)
* OLAP and OLTP support: Can handle both online analytical processing (OLAP) and online transaction processing (OLTP) workloads.
*OLAP and OLTP support: Can handle both online analytical processing (OLAP) and online transaction processing (OLTP) workloads.
* Compression: Lots of compression formats are supported like bit-packing and dictionary encoding
*Compression: Lots of compression formats are supported like bit-packing and dictionary encoding
* Scalability: Designed to scale on a single node with huge NUMA memory
* Scalability: Designed to scale on a single node with huge NUMA memory
* Adjustable persistency: Decide whether you want to persist a table or not or to just keep snapshots of a period of time
*Adjustable persistency: Decide whether you want to persist a table or not or to just keep snapshots of a period of time
[[File:MemCP Port.png|frameless]]
 
<youtube>g29FR4Jwius</youtube>
 
https://www.youtube.com/watch?v=g29FR4Jwius
 
===Navigation===
 
====Introduction====
*[[What is OLTP and OLAP]]
*[[History of the MemCP project]]
*[[Hardware Requirements]]
*[[Persistency and Performance Guarantees]]
*[[Current Status and Open Issues]]
 
====Getting Started====
*[[Install MemCP with Docker|With Docker]]
*[[With Singularity]]
*[[Compile MemCP from Source|Build from Source]]
*[[Contributing]]
*[[Introduction to Scheme]]
 
====Administration====
 
* [[Deployment]]
* [[Migration from MySQL and PostgreSQL]]
* [[Settings]]
*[[Process Hibernation]]
*[[Performance Measurement]]
*[[MemCP Console]]


=== Navigation ===
====Frontends====


==== Introduction ====
=====SQL Frontend=====  
* [[What is OLTP and OLAP]]
*[[Supported SQL]]
* [[History of the MemCP project]]
*[[Advanced SQL Tutorial]]
* [[Hardware Requirements]]
*[[SQL over REST]]
* [[Persistency and Performance Guarantees]]
*[[Database Tools compatibility with MemCP|Supported Tooling]]
* [[Current Status and open issues]]
*[[How SQL Operators are implemented on MemCP]]


==== Getting Started ====
=====RDF Frontend=====  
* [[Install MemCP with Docker|With Docker]]
*[[Introduction to RDF]]
* [[With Singularity]]
*[[Advanced Graph Querying]]
* [[Compile MemCP from Source|Build from Source]]
*[[RDF templating and model driven development]]
* [[Introduction to Scheme]]


==== SQL Frontend ====
=====Custom Frontends=====
* [[Replace MySQL with MemCP]]
* [[SQL over REST]]
* [[Database Tools compatibility with MemCP|Supported Tooling]]
* [[How SQL Operators are implemented on MemCP]]


==== RDF Frontend ====
*[[In-Database WebApps|In-Database WebApps and REST Services]]
* [[Introduction to RDF]]
*[[Websockets in MemCP]]
* [[RDF templating and model driven development]]


==== Internals ====
====Internals====


===== How things work in MemCP =====
=====How things work in MemCP=====  


*[[Databases, Tables and Columns]]
*[[Databases, Tables and Columns]]
* [[Shards, RecordIDs, Main Storage, Delta Storage]]
*[[Shards, RecordIDs, Main Storage, Delta Storage]]
* [[Columnar Storage]]
*[[Columnar Storage]]
* [[Transactions]]
*[[Transactions]]  


===== Optimizations =====
=====Optimizations=====
* [[In-Memory Compression, Columnar Compression Techniques]]
*[[In-Memory Compression, Columnar Compression Techniques]]
* [[Temporary Columns]]
*[[Temporary Columns]]
* [[Data Auto Sharding and Auto Indexing]]
*[[Data Auto Sharding and Auto Indexing]]
* [[Parallel Computing]]
* [[Parallel Computing]]


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=== Further Reading ===
===Further Reading===
[https://github.org/launix-de/memcp MemCP on Github]
[https://github.org/launix-de/memcp MemCP on Github]


==== Scientific ====
====Scientific====


* [https://www.vldb.org/pvldb/vol13/p2649-boncz.pdf VLDB Research Paper]
*[https://www.vldb.org/pvldb/vol13/p2649-boncz.pdf VLDB Research Paper]
* [https://cs.emis.de/LNI/Proceedings/Proceedings241/383.pdf LNI Proceedings Paper]
*[https://cs.emis.de/LNI/Proceedings/Proceedings241/383.pdf LNI Proceedings Paper]
* [https://wwwdb.inf.tu-dresden.de/wp-content/uploads/T_2014_Master_Patrick_Damme.pdf TU Dresden Research Paper]
*[https://wwwdb.inf.tu-dresden.de/wp-content/uploads/T_2014_Master_Patrick_Damme.pdf TU Dresden Research Paper]
* [https://www.dcs.bbk.ac.uk/~dell/teaching/cc/paper/sigmod10/p135-malewicz.pdf Large Graph Algorithms]
*[https://www.dcs.bbk.ac.uk/~dell/teaching/cc/paper/sigmod10/p135-malewicz.pdf Large Graph Algorithms]
* https://wwwdb.inf.tu-dresden.de/research-projects/eris/
*https://wwwdb.inf.tu-dresden.de/research-projects/eris/  


==== How MemCP was built ====
====How MemCP was built====


* [https://launix.de/launix/how-to-balance-a-database-between-olap-and-oltp-workflows/ Balancing OLAP and OLTP Workflows]
*[https://launix.de/launix/how-to-balance-a-database-between-olap-and-oltp-workflows/ Balancing OLAP and OLTP Workflows]
* [https://launix.de/launix/designing-a-programming-language-for-distributed-systems-and-highly-parallel-algorithms/ Designing Programming Languages for Distributed Systems]
*[https://launix.de/launix/designing-a-programming-language-for-distributed-systems-and-highly-parallel-algorithms/ Designing Programming Languages for Distributed Systems]
* [https://launix.de/launix/on-designing-an-interface-for-columnar-in-memory-storage-in-golang/ Columnar Storage Interface in Golang]
*[https://launix.de/launix/on-designing-an-interface-for-columnar-in-memory-storage-in-golang/ Columnar Storage Interface in Golang]
* [https://launix.de/launix/how-in-memory-compression-affects-performance/ Impact of In-Memory Compression on Performance]
*[https://launix.de/launix/how-in-memory-compression-affects-performance/ Impact of In-Memory Compression on Performance]
* [https://launix.de/launix/memory-efficient-indices-for-in-memory-storages/ Memory-Efficient Indices for In-Memory Storages]
*[https://launix.de/launix/memory-efficient-indices-for-in-memory-storages/ Memory-Efficient Indices for In-Memory Storages]
* [https://launix.de/launix/on-compressing-null-values-in-bit-compressed-integer-storages/ Compressing Null Values in Bit-Compressed Integer Storages]
*[https://launix.de/launix/on-compressing-null-values-in-bit-compressed-integer-storages/ Compressing Null Values in Bit-Compressed Integer Storages]
* [https://launix.de/launix/when-the-benchmark-is-too-slow-golang-http-server-performance/ Improving Golang HTTP Server Performance]
*[https://launix.de/launix/when-the-benchmark-is-too-slow-golang-http-server-performance/ Improving Golang HTTP Server Performance]
* [https://launix.de/launix/how-to-benchmark-a-sql-database/ Benchmarking SQL Databases]
*[https://launix.de/launix/how-to-benchmark-a-sql-database/ Benchmarking SQL Databases]
* [https://launix.de/launix/writing-a-sql-parser-in-scheme/ Writing a SQL Parser in Scheme]
*[https://launix.de/launix/writing-a-sql-parser-in-scheme/ Writing a SQL Parser in Scheme]
* [https://launix.de/launix/accessing-memcp-via-scheme/ Accessing memcp via Scheme]
*[https://launix.de/launix/accessing-memcp-via-scheme/ Accessing memcp via Scheme]
* [https://launix.de/launix/memcp-first-sql-query-is-correctly-executed/ First SQL Query in memcp]
*[https://launix.de/launix/memcp-first-sql-query-is-correctly-executed/ First SQL Query in memcp]
* [https://launix.de/launix/sequence-compression-in-in-memory-database-yields-99-memory-savings-and-a-total-of-13/ Sequence Compression in In-Memory Database]
*[https://launix.de/launix/sequence-compression-in-in-memory-database-yields-99-memory-savings-and-a-total-of-13/ Sequence Compression in In-Memory Database]
* [https://launix.de/launix/storing-a-bit-smaller-than-in-one-bit/ Storing Data Smaller Than One Bit]
*[https://launix.de/launix/storing-a-bit-smaller-than-in-one-bit/ Storing Data Smaller Than One Bit]
* [https://www.youtube.com/watch?v=DWg4nx4KVLo memcp Announcement Video]
*[https://www.youtube.com/watch?v=DWg4nx4KVLo memcp Announcement Video]

Latest revision as of 20:14, 20 November 2024

What is memcp?

Webapps.svg

memcp is an open-source, high-performance, columnar in-memory database that can handle both OLAP and OLTP workloads. It provides an alternative to proprietary analytical databases and aims to bring the benefits of columnar storage to the open-source world.

memcp is written in Golang and is designed to be portable and extensible, allowing developers to embed the database into their applications with ease. It is also designed with a focus on scalability and performance, making it a suitable choice for distributed applications.


Features

  • fast: MemCP is built with parallelization in mind. The parallelization pattern is made for minimal overhead.
  • efficient: The average compression ratio is 1:5 (80% memory saving) compared to MySQL/MariaDB
  • modern: MemCP is built for modern hardware with caches, NUMA memory, multicore CPUs, NVMe SSDs
  • versatile: Use it in big mainframes to gain analytical performance, use it in embedded systems to conserve flash lifetime
  • Columnar storage: Stores data column-wise instead of row-wise, which allows for better compression, faster query execution, and more efficient use of memory.
  • In-memory database: Stores all data in memory, which allows for extremely fast query execution.
  • Build fast REST APIs directly in the database (they are faster because there is no network connection / SQL layer in between)
  • OLAP and OLTP support: Can handle both online analytical processing (OLAP) and online transaction processing (OLTP) workloads.
  • Compression: Lots of compression formats are supported like bit-packing and dictionary encoding
  • Scalability: Designed to scale on a single node with huge NUMA memory
  • Adjustable persistency: Decide whether you want to persist a table or not or to just keep snapshots of a period of time

MemCP Port.png

https://www.youtube.com/watch?v=g29FR4Jwius

Navigation

Introduction

Getting Started

Administration

Frontends

SQL Frontend
RDF Frontend
Custom Frontends

Internals

How things work in MemCP
Optimizations


Screenshot from htop.png


Further Reading

MemCP on Github

Scientific

How MemCP was built

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