DBMS > Faircom DB vs. Ignite vs. Milvus vs. SingleStore vs. Tarantool
System Properties Comparison Faircom DB vs. Ignite vs. Milvus vs. SingleStore vs. Tarantool
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| Name | Faircom DB  formerly c-treeACE Xexclude from comparison | Ignite Xexclude from comparison | Milvus Xexclude from comparison | SingleStore former name was MemSQL Xexclude from comparison | Tarantool Xexclude from comparison | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Description | Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs. | Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale. | A DBMS designed for efficient storage of vector data and vector similarity searches | MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type | In-memory computing platform with a flexible data schema for efficiently building high-performance applications | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Primary database model | Key-value store Relational DBMS | Key-value store Relational DBMS | Vector DBMS | Relational DBMS | Document store Key-value store Relational DBMS | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Secondary database models | Document store Spatial DBMS Time Series DBMS Vector DBMS | Spatial DBMS with Tarantool/GIS extension | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Website | www.faircom.com/products/faircom-db | ignite.apache.org | milvus.io | www.singlestore.com | www.tarantool.io | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Technical documentation | docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html | apacheignite.readme.io/docs | milvus.io/docs/overview.md | docs.singlestore.com | www.tarantool.io/en/doc | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Developer | FairCom Corporation | Apache Software Foundation | SingleStore Inc. | VK | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Initial release | 1979 | 2015 | 2019 | 2013 | 2008 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Current release | V12, November 2020 | Apache Ignite 2.6 | 2.4.4, May 2024 | 8.5, January 2024 | 2.10.0, May 2022 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| License Commercial or Open Source | commercial Restricted, free version available | Open Source Apache 2.0 | Open Source Apache Version 2.0 | commercial free developer edition available | Open Source BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Cloud-based only Only available as a cloud service | no | no | no | no | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| DBaaS offerings (sponsored links) Database as a Service Providers of DBaaS offerings, please contact us to be listed. | Zilliz Cloud – Cloud-native service for Milvus | SingleStoreDB Cloud: The world's fastest, modern cloud database for both operational (OLTP) and analytical (OLAP) workloads. Available instantly with multi-cloud and hybrid-cloud capabilities | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Implementation language | ANSI C, C++ | C++, Java, .Net | C++, Go | C++, Go | C and C++ | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Server operating systems | AIX FreeBSD HP-UX Linux NetBSD OS X QNX SCO Solaris VxWorks Windows easily portable to other OSs | Linux OS X Solaris Windows | Linux macOS 10.14 or laterWindows with WSL 2 enabled | Linux 64 bit version required | BSD Linux macOS | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data scheme | schema free, schema optional, schema required, partial schema, | yes | yes | Flexible data schema: relational definition for tables with ability to store json-like documents in columns | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Typing predefined data types such as float or date | yes, ANSI SQL Types, JSON, typed binary structures | yes | Vector, Numeric and String | yes | string, double, decimal, uuid, integer, blob, boolean, datetime | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| XML support Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT. | no | yes | no | no | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Secondary indexes | yes | yes | no | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| SQL Support of SQL | yes, ANSI SQL with proprietary extensions | ANSI-99 for query and DML statements, subset of DDL | no | yes but no triggers and foreign keys | Full-featured ANSI SQL support | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| APIs and other access methods | ADO.NET Direct SQL JDBC JPA ODBC RESTful HTTP/JSON API RESTful MQTT/JSON API RPC | HDFS API Hibernate JCache JDBC ODBC Proprietary protocol RESTful HTTP API Spring Data | RESTful HTTP API | Cluster Management API as HTTP Rest and CLI HTTP API JDBC MongoDB API ODBC | Open binary protocol | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Supported programming languages | .Net C C# C++ Java JavaScript (Node.js and browser) PHP Python Visual Basic | C# C++ Java PHP Python Ruby Scala | C++ Go Java JavaScript (Node.js) Python | Bash C C# Java JavaScript (Node.js) Python | C C# C++ Erlang Go Java JavaScript Lua Perl PHP Python Rust | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Server-side scripts Stored procedures | yes .Net, JavaScript, C/C++ | yes (compute grid and cache interceptors can be used instead) | no | yes | Lua, C and SQL stored procedures | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Triggers | yes | yes (cache interceptors and events) | no | no | yes, before/after data modification events, on replication events, client session events | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Partitioning methods Methods for storing different data on different nodes | File partitioning, horizontal partitioning, sharding Customizable business rules for table partitioning | Sharding | Sharding | Sharding hash partitioning | Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Replication methods Methods for redundantly storing data on multiple nodes | yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution). | yes (replicated cache) | Source-replica replication stores two copies of each physical data partition on two separate nodes | Asynchronous replication with multi-master option Configurable replication topology (full-mesh, chain, star) Synchronous quorum replication (with Raft) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| MapReduce Offers an API for user-defined Map/Reduce methods | no | yes (compute grid and hadoop accelerator) | no | no can define user-defined aggregate functions for map-reduce-style calculations | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Consistency concepts Methods to ensure consistency in a distributed system | Eventual Consistency Immediate Consistency Tunable consistency per server, database, table, and transaction | Immediate Consistency | Bounded Staleness Eventual Consistency Immediate Consistency Session Consistency Tunable Consistency | Immediate Consistency | Casual consistency across sharding partitions Eventual consistency within replicaset partition when using asyncronous replicationImmediate Consistency within single instance Sequential consistency including linearizable read within replicaset partition when using Raft | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Foreign keys Referential integrity | yes | no | no | no | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Transaction concepts Support to ensure data integrity after non-atomic manipulations of data | tunable from ACID to Eventually Consistent | ACID | no | ACID | ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Concurrency Support for concurrent manipulation of data | yes | yes | yes | yes, multi-version concurrency control (MVCC) | yes, cooperative multitasking | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Durability Support for making data persistent | Yes, tunable from durable to delayed durability to in-memory | yes | yes | yes All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log. | yes, write ahead logging | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| In-memory capabilities Is there an option to define some or all structures to be held in-memory only. | yes | yes | yes | yes | yes, full featured in-memory storage engine with persistence | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| User concepts Access control | Fine grained access rights according to SQL-standard with additional protections for files | Security Hooks for custom implementations | Role based access control and fine grained access rights | Fine grained access control via users, groups and roles | Access Control Lists Mutual TLS authentication for Tarantol Enterprise Password based authentication Role-based access control (RBAC) and LDAP for Tarantol Enterprise Users and Roles | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| More information provided by the system vendor | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Faircom DB formerly c-treeACE | Ignite | Milvus | SingleStore former name was MemSQL | Tarantool | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Specific characteristics | Milvus is an open-source and cloud-native vector database built for production-ready... » more | SingleStore offers a fully-managed , distributed, highly-scalable SQL database designed... » more | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competitive advantages | Highly available, versatile, and robust with millisecond latency. Supports batch... » more | SingleStore’s competitive advantages include: Easy and Simplified Architecture with... » more | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Typical application scenarios | RAG: retrieval augmented generation Video media : video understanding, video deduplication.... » more | Driving Fast Analytics: SingleStore delivers the fastest and most scalable reporting... » more | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Key customers | Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,... » more | IEX Cloud : Improves Financial Data Distribution Speed 15x with Singlestore DB Comcast,... » more | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Market metrics | As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise... » more | Customers in various industries worldwide including US and International Industry... » more | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Licensing and pricing models | Milvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed... » more | F ree Tier and Enterprise Edition » more | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| More resources | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Faircom DB formerly c-treeACE | Ignite | Milvus | SingleStore former name was MemSQL | Tarantool | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| DB-Engines blog posts | Vector databases | Turbocharge Your Application Development Using WebAssembly With SingleStoreDB Cloud-Based Analytics With SingleStoreDB SingleStore: The Increasing Momentum of Multi-Model Database Systems | Data processing speed and reliability: in-memory synchronous replication FairCom Unveils New Look, FairCom DB v13: Introducing 'DB Made Simple' FairCom kicks off new era of database technology USA - English provided by Google News GridGain Announces Call for Speakers for Virtual Apache Ignite Summit 2024 GridGain Showcases Power of Apache Ignite at Community Over Code Conference Apache Ignite: Distributed Database Apache Ignite: An Overview What is Apache Ignite? How is Apache Ignite Used? provided by Google News How NVIDIA GPU Acceleration Supercharged Milvus Vector Database AI-Powered Search Engine With Milvus Vector Database on Vultr Milvus 2.4 Unveils Game-Changing Features for Enhanced Vector Search Zilliz Unveils Game-Changing Features for Vector Search IBM watsonx.data’s integrated vector database: unify, prepare, and deliver your data for AI provided by Google News Building a Modern Database: Nikita Shamgunov on Postgres and Beyond SingleStore CEO sees little future for purpose-built vector databases SingleStore Announces Real-time Data Platform to Further Accelerate AI, Analytics and Application Development SingleStore adds indexed vector search to Pro Max release for faster AI work – Blocks and Files SingleStore, Snowflake Help Customers Build Enterprise-Ready Generative AI Apps provided by Google News Tarantool Announces New Enterprise Version With Enhanced Scaling and Monitoring Capabilities Deploying Tarantool Cartridge applications with zero effort (Part 1) VShard — horizontal scaling in Tarantool Accelerating PHP connectors for Tarantool using Async, Swoole, and Parallel provided by Google News | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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