DBMS > InfinityDB vs. InfluxDB vs. Microsoft Azure Data Explorer vs. Tarantool vs. YugabyteDB
System Properties Comparison InfinityDB vs. InfluxDB vs. Microsoft Azure Data Explorer vs. Tarantool vs. YugabyteDB
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Name | InfinityDB Xexclude from comparison | InfluxDB Xexclude from comparison | Microsoft Azure Data Explorer Xexclude from comparison | Tarantool Xexclude from comparison | YugabyteDB Xexclude from comparison | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Description | A Java embedded Key-Value Store which extends the Java Map interface | DBMS for storing time series, events and metrics | Fully managed big data interactive analytics platform | In-memory computing platform with a flexible data schema for efficiently building high-performance applications | High-performance distributed SQL database for global, internet-scale applications. Wire and feature compatible with PostgreSQL. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Primary database model | Key-value store | Time Series DBMS | Relational DBMS column oriented | Document store Key-value store Relational DBMS | Relational DBMS | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Secondary database models | Spatial DBMS with GEO package | Document store If a column is of type dynamic docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types/dynamic then it's possible to add arbitrary JSON documents in this cell Event Store this is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps) Spatial DBMS Search engine support for complex search expressions docs.microsoft.com/en-us/azure/kusto/query/parseoperator FTS, Geospatial docs.microsoft.com/en-us/azure/kusto/query/geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine Time Series DBMS see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis | Spatial DBMS with Tarantool/GIS extension | Document store Wide column store | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Website | boilerbay.com | www.influxdata.com/products/influxdb-overview | azure.microsoft.com/services/data-explorer | www.tarantool.io | www.yugabyte.com | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Technical documentation | boilerbay.com/infinitydb/manual | docs.influxdata.com/influxdb | docs.microsoft.com/en-us/azure/data-explorer | www.tarantool.io/en/doc | docs.yugabyte.com github.com/yugabyte/yugabyte-db | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Developer | Boiler Bay Inc. | Microsoft | VK | Yugabyte Inc. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Initial release | 2002 | 2013 | 2019 | 2008 | 2017 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Current release | 4.0 | 2.7.6, April 2024 | cloud service with continuous releases | 2.10.0, May 2022 | 2.19, September 2023 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
License Commercial or Open Source | commercial | Open Source MIT-License; commercial enterprise version available | commercial | Open Source BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise | Open Source Apache 2.0 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Cloud-based only Only available as a cloud service | no | no | yes | no | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
DBaaS offerings (sponsored links) Database as a Service Providers of DBaaS offerings, please contact us to be listed. | YugabyteDB Managed is the fully managed database-as-a-service offering of YugabyteDB. Get started quickly, and effortlessly ensure continuous availability and limitless scale of your cloud native applications. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Implementation language | Java | Go | C and C++ | C and C++ | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Server operating systems | All OS with a Java VM | Linux OS X through Homebrew | hosted | BSD Linux macOS | Linux OS X | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Data scheme | yes nested virtual Java Maps, multi-value, logical ‘tuple space’ runtime Schema upgrade | schema-free | Fixed schema with schema-less datatypes (dynamic) | Flexible data schema: relational definition for tables with ability to store json-like documents in columns | depending on used data model | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Typing predefined data types such as float or date | yes all Java primitives, Date, CLOB, BLOB, huge sparse arrays | Numeric data and Strings | yes bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types | string, double, decimal, uuid, integer, blob, boolean, datetime | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 | no | yes | no | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Secondary indexes | no manual creation possible, using inversions based on multi-value capability | no | all fields are automatically indexed | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
SQL Support of SQL | no | SQL-like query language | Kusto Query Language (KQL), SQL subset | Full-featured ANSI SQL support | yes, PostgreSQL compatible | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
APIs and other access methods | Access via java.util.concurrent.ConcurrentNavigableMap Interface Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm) | HTTP API JSON over UDP | Microsoft SQL Server communication protocol (MS-TDS) RESTful HTTP API | Open binary protocol | JDBC YCQL, an SQL-based flexible-schema API with its roots in Cassandra Query Language YSQL - a fully relational SQL API that is wire compatible with the SQL language in PostgreSQL | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Supported programming languages | Java | .Net Clojure Erlang Go Haskell Java JavaScript JavaScript (Node.js) Lisp Perl PHP Python R Ruby Rust Scala | .Net Go Java JavaScript (Node.js) PowerShell Python R | C C# C++ Erlang Go Java JavaScript Lua Perl PHP Python Rust | C C# C++ Go Java JavaScript (Node.js) PHP Python Ruby Rust Scala | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Server-side scripts Stored procedures | no | no | Yes, possible languages: KQL, Python, R | Lua, C and SQL stored procedures | yes sql, plpgsql, C | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Triggers | no | no | yes see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy | yes, before/after data modification events, on replication events, client session events | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Partitioning methods Methods for storing different data on different nodes | none | Sharding in enterprise version only | Sharding Implicit feature of the cloud service | Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime. | Hash and Range Sharding, row-level geo-partitioning | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Replication methods Methods for redundantly storing data on multiple nodes | none | selectable replication factor in enterprise version only | yes Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant. | Asynchronous replication with multi-master option Configurable replication topology (full-mesh, chain, star) Synchronous quorum replication (with Raft) | Based on Raft distributed consensus protocol, minimum 3 replicas for continuous availability | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
MapReduce Offers an API for user-defined Map/Reduce methods | no | no | Spark connector (open source): github.com/Azure/azure-kusto-spark | no | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Consistency concepts Methods to ensure consistency in a distributed system | Immediate Consistency READ-COMMITTED or SERIALIZED | Eventual Consistency Immediate Consistency | Casual consistency across sharding partitions Eventual consistency within replicaset partition when using asyncronous replication Immediate Consistency within single instance Sequential consistency including linearizable read within replicaset partition when using Raft | Strong consistency on writes and tunable consistency on reads | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Foreign keys Referential integrity | no manual creation possible, using inversions based on multi-value capability | no | no | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Transaction concepts Support to ensure data integrity after non-atomic manipulations of data | ACID Optimistic locking for transactions; no isolation for bulk loads | no | no | ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions | Distributed ACID with Serializable & Snapshot Isolation. Inspired by Google Spanner architecture. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Concurrency Support for concurrent manipulation of data | yes | yes | yes | yes, cooperative multitasking | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Durability Support for making data persistent | yes | yes | yes | yes, write ahead logging | yes based on RocksDB | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
In-memory capabilities Is there an option to define some or all structures to be held in-memory only. | no | yes Depending on used storage engine | no | yes, full featured in-memory storage engine with persistence | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
User concepts Access control | no | simple rights management via user accounts | Azure Active Directory Authentication | 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 | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
More information provided by the system vendor | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
InfinityDB | InfluxDB | Microsoft Azure Data Explorer | Tarantool | YugabyteDB | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Specific characteristics | InfluxData is the creator of InfluxDB , the open source time series database. It... » more | YugabyteDB is an open source distributed SQL database for cloud native transactional... » more | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Competitive advantages | Time to Value InfluxDB is available in all the popular languages and frameworks,... » more | PostgreSQL compatible: Get instantly productive with a PostgreSQL compatible RDBMS.... » more | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Typical application scenarios | IoT & Sensor Monitoring Developers are witnessing the instrumentation of every available... » more | Systems of record and engagement for cloud native applications that require resilience,... » more | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Key customers | InfluxData has more than 1,900 paying customers, including customers include MuleSoft,... » more | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Market metrics | Fastest-growing database to drive 27,500 GitHub stars Over 750,000 daily active instances » more | 2 Million+ lifetime clusters deployed, 6.5K+ GitHub stars, 7K YugabyteDB Community... » more | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Licensing and pricing models | Open source core with closed source clustering available either on-premise or on... » more | Apache 2.0 license for the database » more | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
News | Introduction to Apache Iceberg Converting Timestamp to Date in Java A Detailed Guide to C# TimeSpan The Final Frontier: Using InfluxDB on the International Space Station Getting the Current Time in C#: A Guide | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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More resources | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
InfinityDB | InfluxDB | Microsoft Azure Data Explorer | Tarantool | YugabyteDB | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
DB-Engines blog posts | Why Build a Time Series Data Platform? Time Series DBMS are the database category with the fastest increase in popularity Time Series DBMS as a new trend? | Data processing speed and reliability: in-memory synchronous replication | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Recent citations in the news | Run and manage open source InfluxDB databases with Amazon Timestream | Amazon Web Services Amazon Timestream: Managed InfluxDB for Time Series Data InfluxData Collaborating with AWS to Bring InfluxDB and Time Series Analytics to Developers Around the World How the FDAP Stack Gives InfluxDB 3.0 Real-Time Speed, Efficiency AWS and InfluxData partner to offer managed time series database Timestream for InfluxDB provided by Google News | General availability: Azure Data Explorer adds new geospatial capabilities | Azure updates Public Preview: Azure Data Explorer connector for Apache Flink | Azure updates Azure Data Explorer: Log and telemetry analytics benchmark Providing modern data transfer and storage service at Microsoft with Microsoft Azure - Inside Track Blog Introducing Microsoft Fabric: The data platform for the era of AI | Microsoft Azure Blog provided by Google News | Tarantool Announces New Enterprise Version With Enhanced Scaling and Monitoring Capabilities Deploying Tarantool Cartridge applications with zero effort (Part 1) TaranHouse: New Big Data Warehouse Announced by Tarantool Deploying Tarantool Cartridge applications with zero effort (Part 2) Тarantool Cartridge: Sharding Lua Backend in Three Lines provided by Google News | Yugabyte Achieves PCI DSS Level 1 Compliance, Validating Secure and Scalable Distributed PostgreSQL for ... YugabyteDB Becomes First Distributed SQL Database Vendor to Complete CIS Benchmark Yugabyte announced details and theme for Distributed SQL Summit Asia The surprising link between Formula One and enterprise PostgreSQL optimisation Yugabyte adds multiregion Kubernetes support to YugabyteDB 2.18 provided by Google News |
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