DBMS > Ignite vs. Microsoft Azure Data Explorer vs. PostgreSQL vs. Spark SQL vs. Tarantool
Vergleich der Systemeigenschaften Ignite vs. Microsoft Azure Data Explorer vs. PostgreSQL vs. Spark SQL vs. Tarantool
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| Name | Ignite Xaus Vergleich ausschliessen | Microsoft Azure Data Explorer Xaus Vergleich ausschliessen | PostgreSQL Xaus Vergleich ausschliessen | Spark SQL Xaus Vergleich ausschliessen | Tarantool Xaus Vergleich ausschliessen | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | 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. | Fully managed big data interactive analytics platform | Weit verbreitetes, allgemein einsetzbares Open Source RDBMS  Entwickelt als objektorientiertes DBMS, im Laufe der Zeit um 'Standards' wie SQL erweitert | Spark SQL ist ein Spark-Modul für die Verarbeitung strukturierter Daten | In-memory computing platform with a flexible data schema for efficiently building high-performance applications | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Primäres Datenbankmodell | Key-Value Store Relational DBMS | Relational DBMS column oriented | Relational DBMS mit objektorientierten Erweiterungen, z.B.: benutzerdefinierte Datentypen/Funktionen und Vererbung. Key/Value handling mit hstore Modul. | Relational DBMS | Document Store Key-Value Store Relational DBMS | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Sekundäre Datenbankmodelle | 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 Suchmaschine 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 Vektor DBMS with pgvector extension | Spatial DBMS with Tarantool/GIS extension | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Website | ignite.apache.org | azure.microsoft.com/services/data-explorer | www.postgresql.org | spark.apache.org/sql | www.tarantool.io | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Technische Dokumentation | apacheignite.readme.io/docs | docs.microsoft.com/en-us/azure/data-explorer | www.postgresql.org/docs | spark.apache.org/docs/latest/sql-programming-guide.html | www.tarantool.io/en/doc | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Entwickler | Apache Software Foundation | Microsoft | PostgreSQL Global Development Group www.postgresql.org/developer | Apache Software Foundation | VK | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Erscheinungsjahr | 2015 | 2019 | 1989 1989: Postgres, 1996: PostgreSQL | 2014 | 2008 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Aktuelle Version | Apache Ignite 2.6 | cloud service with continuous releases | 16.3, Mai 2024 | 3.5.0 ( 2.13), September 2023 | 2.10.0, Mai 2022 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lizenz Commercial or Open Source | Open Source Apache 2.0 | kommerziell | Open Source BSD | Open Source Apache 2.0 | Open Source BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Ausschließlich ein Cloud-Service Nur als Cloud-Service verfügbar | nein | ja | nein | nein | nein | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Implementierungssprache | C++, Java, .Net | C | Scala | C und C++ | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Server Betriebssysteme | Linux OS X Solaris Windows | gehostet | FreeBSD HP-UX Linux NetBSD OpenBSD OS X Solaris Unix Windows | Linux OS X Windows | BSD Linux macOS | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Datenschema | ja | Fixed schema with schema-less datatypes (dynamic) | ja | ja | Flexible data schema: relational definition for tables with ability to store json-like documents in columns | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Typisierung vordefinierte Datentypen, z.B. float oder date | ja | ja bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types | ja | ja | string, double, decimal, uuid, integer, blob, boolean, datetime | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| XML Unterstützung Verarbeitung von Daten in XML Format, beispielsweise Speicherung von XML-Strukturen und/oder Unterstützung von XPath, XQuery, XSLT | ja | ja | ja specific XML-type available, but no XML query functionality. | nein | nein | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Sekundärindizes | ja | all fields are automatically indexed | ja | nein | ja | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| SQL Support of SQL | ANSI-99 for query and DML statements, subset of DDL | Kusto Query Language (KQL), SQL subset | ja Standard mit zahlreichen Erweiterungen | SQL-like DML and DDL statements | Full-featured ANSI SQL support | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| APIs und andere Zugriffskonzepte | HDFS API Hibernate JCache JDBC ODBC Proprietäres Protokoll RESTful HTTP API Spring Data | Microsoft SQL Server communication protocol (MS-TDS) RESTful HTTP API | ADO.NET JDBC native C library ODBC streaming API for large objects | JDBC ODBC | Open binary protocol | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Unterstützte Programmiersprachen | C# C++ Java PHP Python Ruby Scala | .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 | C C# C++ Erlang Go Java JavaScript Lua Perl PHP Python Rust | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Server-seitige Scripts Stored Procedures | yes (compute grid and cache interceptors can be used instead) | Yes, possible languages: KQL, Python, R | benutzerdefinierte Funktionen realisiert z.B. mit spezifischer Sprache PL/pgSQL, aber auch andere Sprachen möglich (Perl, Python, Tcl, etc.) | nein | Lua, C and SQL stored procedures | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Triggers | yes (cache interceptors and events) | ja see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy | ja | nein | ja | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Partitionierungsmechanismen Methoden zum Speichern von unterschiedlichen Daten auf unterschiedlichen Knoten | Sharding | Sharding Implicit feature of the cloud service | partitioning by range, list and (since PostgreSQL 11) by hash | yes, utilizing Spark Core | Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Replikationsmechanismen Methoden zum redundanten Speichern von Daten auf mehreren Knoten | yes (replicated cache) | ja Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant. | Source-Replica Replikation andere Varianten durch Verwendung von 3rd party - tools | keine | Asynchronous replication with multi-master option Configurable replication topology (full-mesh, chain, star) Synchronous quorum replication (with Raft) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| MapReduce Bietet ein API für Map/Reduce Operationen | yes (compute grid and hadoop accelerator) | Spark connector (open source): github.com/Azure/azure-kusto-spark | nein | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Konsistenzkonzept Methoden zur Sicherstellung der Konsistenz in einem verteilten System | Immediate Consistency | Eventual Consistency Immediate 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 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Fremdschlüssel referenzielle Integrität | nein | nein | ja | nein | yes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Transaktionskonzept Unterstützung zur Sicherstellung der Datenintegrität bei nicht-atomaren Datenmanipulationen | ACID | nein | ACID | nein | ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Concurrency Unterstützung von gleichzeitig ausgeführten Datenmanipulationen | ja | ja | ja | ja | ja | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Durability Dauerhafte Speicherung der Daten | ja | ja | ja | ja | yes, write ahead logging | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| In-Memory Unterstützung Gibt es Möglichkeiten einige oder alle Strukturen nur im Hauptspeicher zu halten | ja | nein | nein | nein | yes, full featured in-memory storage engine with persistence | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Berechtigungskonzept Zugriffskontrolle | Security Hooks for custom implementations | Azure Active Directory Authentication | Benutzer mit feingranularem Berechtigungskonzept entsprechend SQL-Standard | nein | 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Ignite | Microsoft Azure Data Explorer | PostgreSQL | Spark SQL | Tarantool | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| DB-Engines Blog Posts | PostgreSQL is the DBMS of the Year 2023 Snowflake is the DBMS of the Year 2022, defending the title from last year Snowflake is the DBMS of the Year 2021 | Data processing speed and reliability: in-memory synchronous replication Monitoring PostgreSQL with Redgate Monitor GridGain Announces Call for Speakers for Virtual Apache Ignite Summit 2024 Apache Ignite: An Overview GridGain Releases Conference Schedule for Virtual Apache Ignite Summit 2023 What is Apache Ignite? How is Apache Ignite Used? Real-time in-memory OLTP and Analytics with Apache Ignite on AWS | Amazon Web Services bereitgestellt von Google News Azure Data Explorer: Log and telemetry analytics benchmark Providing modern data transfer and storage service at Microsoft with Microsoft Azure - Inside Track Blog Controlling costs in Azure Data Explorer using down-sampling and aggregation Individually great, collectively unmatched: Announcing updates to 3 great Azure Data Services Analytics in Azure is up to 14x faster and costs 94% less than other cloud providers. Why go anywhere else? bereitgestellt von Google News At Build, Microsoft Fabric, PostgreSQL and Cosmos DB get AI enhancements Nutanix partners with EDB to fit database service for AI – Blocks and Files Nutanix and EDB Partner to Deliver a Modern Data Platform Let PostgreSQL Pick An Index For You Continuously replicate Amazon DynamoDB changes to Amazon Aurora PostgreSQL using AWS Lambda | Amazon ... bereitgestellt von Google News Use Amazon Athena with Spark SQL for your open-source transactional table formats | Amazon Web Services What is Apache Spark? The big data platform that crushed Hadoop Cracking the Apache Spark Interview: 80+ Top Questions and Answers for 2024 Performant IPv4 Range Spark Joins | by Jean-Claude Cote 18 Top Big Data Tools and Technologies to Know About in 2024 bereitgestellt von Google News Deploying Tarantool Cartridge applications with zero effort (Part 1) VShard — horizontal scaling in Tarantool Accelerating PHP connectors for Tarantool using Async, Swoole, and Parallel bereitgestellt von Google News | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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