DBMS > Ignite vs. Informix vs. Microsoft Azure Data Explorer vs. PostgreSQL vs. Spark SQL
System Properties Comparison Ignite vs. Informix vs. Microsoft Azure Data Explorer vs. PostgreSQL vs. Spark SQL
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| Name | Ignite Xexclude from comparison | Informix Xexclude from comparison | Microsoft Azure Data Explorer Xexclude from comparison | PostgreSQL Xexclude from comparison | Spark SQL Xexclude from comparison | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Description | 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 secure embeddable database from IBM, positioned besides IBM Db2 as a relatively low-cost product optimized for OLTP and Internet of Things data | Fully managed big data interactive analytics platform | Widely used open source RDBMS  Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL | Spark SQL is a component on top of 'Spark Core' for structured data processing | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Primary database model | Key-value store Relational DBMS | Relational DBMS Since Version 12.10 support for JSON/BSON datatypes compatible with MongoDB | Relational DBMS column oriented | Relational DBMS with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module. | Relational DBMS | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Secondary database models | Document store Spatial DBMS Time Series DBMS with Informix TimeSeries Extension | 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 engineTime Series DBMS see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis | Document store Graph DBMS with Apache AgeSpatial DBMS Vector DBMS with pgvector extension | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Website | ignite.apache.org | www.ibm.com/products/informix | azure.microsoft.com/services/data-explorer | www.postgresql.org | spark.apache.org/sql | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Technical documentation | apacheignite.readme.io/docs | informix.hcldoc.com www.ibm.com/support/knowledgecenter/SSGU8G/welcomeIfxServers.html | docs.microsoft.com/en-us/azure/data-explorer | www.postgresql.org/docs | spark.apache.org/docs/latest/sql-programming-guide.html | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Developer | Apache Software Foundation | IBM, HCL Technologies Effective May 1st, 2017, HCL took on development, technical support, and product management teams, and works jointly with IBM on product strategy, marketing, and sales. | Microsoft | PostgreSQL Global Development Group www.postgresql.org/developer | Apache Software Foundation | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Initial release | 2015 | 1984 | 2019 | 1989 1989: Postgres, 1996: PostgreSQL | 2014 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Current release | Apache Ignite 2.6 | 14.10.FC5, November 2020 | cloud service with continuous releases | 16.3, May 2024 | 3.5.0 ( 2.13), September 2023 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| License Commercial or Open Source | Open Source Apache 2.0 | commercial free developer edition available | commercial | Open Source BSD | Open Source Apache 2.0 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Cloud-based only Only available as a cloud service | no | no | yes | no | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Implementation language | C++, Java, .Net | C, C++ and Java | C | Scala | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Server operating systems | Linux OS X Solaris Windows | AIX HP-UX Linux macOS Solaris Windows | hosted | FreeBSD HP-UX Linux NetBSD OpenBSD OS X Solaris Unix Windows | Linux OS X Windows | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data scheme | yes | yes | Fixed schema with schema-less datatypes (dynamic) | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Typing predefined data types such as float or date | yes | yes Since Version 12.10 support for JSON/BSON datatypes | yes bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types | yes | 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. | yes | yes | yes specific XML-type available, but no XML query functionality. | no | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Secondary indexes | yes | yes | all fields are automatically indexed | yes | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| SQL Support of SQL | ANSI-99 for query and DML statements, subset of DDL | yes | Kusto Query Language (KQL), SQL subset | yes standard with numerous extensions | SQL-like DML and DDL statements | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| APIs and other access methods | HDFS API Hibernate JCache JDBC ODBC Proprietary protocol RESTful HTTP API Spring Data | JDBC JSON API MongoDB compatibleMQTT (Message Queue Telemetry Transport) ODBC RESTful HTTP API | Microsoft SQL Server communication protocol (MS-TDS) RESTful HTTP API | ADO.NET JDBC native C library ODBC streaming API for large objects | JDBC ODBC | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Supported programming languages | C# C++ Java PHP Python Ruby Scala | .Net C C++ Java JavaScript (Node.js) PHP Python Ruby | .Net Go Java JavaScript (Node.js) PowerShell Python R | .Net C C++ Delphi Java JDBCJavaScript (Node.js) Perl PHP Python Tcl | Java Python R Scala | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Server-side scripts Stored procedures | yes (compute grid and cache interceptors can be used instead) | yes | Yes, possible languages: KQL, Python, R | user defined functions realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc. | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Triggers | yes (cache interceptors and events) | yes | yes see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy | yes | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Partitioning methods Methods for storing different data on different nodes | Sharding | Sharding | Sharding Implicit feature of the cloud service | partitioning by range, list and (since PostgreSQL 11) by hash | yes, utilizing Spark Core | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Replication methods Methods for redundantly storing data on multiple nodes | yes (replicated cache) | Multi-source replication Source-replica replication | yes Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant. | Source-replica replication other methods possible by using 3rd party extensions | none | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| MapReduce Offers an API for user-defined Map/Reduce methods | yes (compute grid and hadoop accelerator) | no | Spark connector (open source): github.com/Azure/azure-kusto-spark | no | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Consistency concepts Methods to ensure consistency in a distributed system | Immediate Consistency | Immediate Consistency | Eventual Consistency Immediate Consistency | Immediate Consistency | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Foreign keys Referential integrity | no | yes | no | yes | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Transaction concepts Support to ensure data integrity after non-atomic manipulations of data | ACID | ACID | no | ACID | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Concurrency Support for concurrent manipulation of data | yes | yes | yes | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Durability Support for making data persistent | yes | yes | yes | yes | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| In-memory capabilities Is there an option to define some or all structures to be held in-memory only. | yes | yes | no | no | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| User concepts Access control | Security Hooks for custom implementations | Users with fine-grained authentication, authorization, and auditing controls | Azure Active Directory Authentication | fine grained access rights according to SQL-standard | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Ignite | Informix | Microsoft Azure Data Explorer | PostgreSQL | Spark SQL | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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