|tagger||Maxim Muzafarov <email@example.com>||Mon Dec 28 14:58:34 2020 +0300|
|author||ymolochkov <firstname.lastname@example.org>||Tue Dec 22 19:32:06 2020 +0300|
|committer||GitHub <email@example.com>||Tue Dec 22 08:32:06 2020 -0800|
IGNITE-13884 Merged docs into 2.9.1 from 2.9 branch with updates (#8598) * IGNITE-7595: new Ignite docs (returning the original changes after fixing licensing issues) (cherry picked from commit 073488ac97517bbaad9f6b94b781fc404646f191) * IGNITE-13574: add license headers for some imported files of the Ignite docs (#8361) * Added a proper license header to some files used by the docs. * Enabled the defaultLicenseMatcher for the license checker. (cherry picked from commit d928fb8576b22dffbfce90a5541e67dc6cbfe410) * ignite docs: updated a couple of contribution instructions (cherry picked from commit 9e8da702068b1232789f8f9f93680f2c6d69ed16) * IGNITE-13527: replace some references to the readme.io docs with the references to the new pages. The job will be finished as part of IGNITE-13586 (cherry picked from commit 7399ae64972cc097c48769cb5e2d9622ce7f7234) * ignite docs: fixed broken lings to the SQLLine page (cherry picked from commit faf4f467e964d478b3d99b94d43d32430a7e88f0) * IGNITE-13615 Update .NET thin client feature set documentation * IGNITE-13652 Wrong GitHub link for Apache Ignite With Spring Data/Example (#8420) * ignite docs: updated the TcpDiscovery.soLinger documentation * IGNITE-13663 : Represent in the documenttion affection of several node addresses on failure detection v2. (#8424) * ignite docs: set the latest spring-data artifact id after receiving user feedback * IGNITE-12951 Update documents for migrated extensions - Fixes #8488. Signed-off-by: samaitra <firstname.lastname@example.org> (cherry picked from commit 15a5da500c08948ee081533af97a9f1c2c8330f8) * ignite docs: fixing a broken documentation link * ignite docs: updated the index page with quick links to the APIs and examples * ignite docs: fixed broken links and updated the C++ API header * ignite docs: fixed case of GitHub * IGNITE-13876 Updated documentation for 2.9.1 release (#8592) (cherry picked from commit e74cf6ba8711338ed48dd01d1efe12505977f63f) Co-authored-by: Denis Magda <email@example.com> Co-authored-by: Pavel Tupitsyn <firstname.lastname@example.org> Co-authored-by: Denis Garus <email@example.com> Co-authored-by: Vladsz83 <firstname.lastname@example.org> Co-authored-by: samaitra <email@example.com> Co-authored-by: Nikita Safonov <firstname.lastname@example.org> Co-authored-by: ymolochkov <email@example.com>
Apache Ignite is a horizontally scalable, fault-tolerant distributed in-memory computing platform for building real-time applications that can process terabytes of data with in-memory speed.
Apache Ignite is designed to work with memory, disk, and Intel Optane as active storage tiers. The memory tier allows using DRAM and Intel® Optane™ operating in the Memory Mode for data storage and processing needs. The disk tier is optional with the support of two options -- you can persist data in an external database or keep it in the Ignite native persistence. SSD, Flash, HDD, or Intel Optane operating in the AppDirect Mode can be used as a storage device.
Even though Apache Ignite is broadly used as a caching layer on top of external databases, it comes with its native persistence - a distributed, ACID, and SQL-compliant disk-based store. The native persistence integrates into the Ignite multi-tier storage as a disk tier that can be turned on to let Ignite store more data on disk than it can cache in memory and to enable fast cluster restarts.
Data stored in Ignite is ACID-compliant both in memory and on disk, making Ignite a strongly consistent system. Ignite transactions work across the network and can span multiple servers.
Apache Ignite comes with a ANSI-99 compliant, horizontally scalable, and fault-tolerant SQL engine that allows you to interact with Ignite as with a regular SQL database using JDBC, ODBC drivers, or native SQL APIs available for Java, C#, C++, Python, and other programming languages. Ignite supports all DML commands, including SELECT, UPDATE, INSERT, and DELETE queries as well as a subset of DDL commands relevant for distributed systems.
Apache Ignite Machine Learning is a set of simple, scalable, and efficient tools that allow building predictive machine learning models without costly data transfers. The rationale for adding machine and deep learning to Apache Ignite is quite simple. Today's data scientists have to deal with two major factors that keep ML from mainstream adoption.
High-performance computing (HPC) is the ability to process data and perform complex calculations at high speeds. Using Apache Ignite as a high-performance compute cluster, you can turn a group of commodity machines or a cloud environment into a distributed supercomputer of interconnected Ignite nodes. Ignite enables speed and scale by processing records in memory and reducing network utilization with APIs for data and compute-intensive calculations. Those APIs implement the MapReduce paradigm and allow you to run arbitrary tasks across the cluster of nodes.