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From sboi...@apache.org
Subject [16/26] ignite git commit: Updated Ignite description
Date Mon, 18 Sep 2017 08:43:16 GMT
Updated Ignite description

Project: http://git-wip-us.apache.org/repos/asf/ignite/repo
Commit: http://git-wip-us.apache.org/repos/asf/ignite/commit/cb4195fa
Tree: http://git-wip-us.apache.org/repos/asf/ignite/tree/cb4195fa
Diff: http://git-wip-us.apache.org/repos/asf/ignite/diff/cb4195fa

Branch: refs/heads/ignite-6149
Commit: cb4195fabb637c405b40fd613f1abbeae93a70bd
Parents: 1b5ca44
Author: Denis Magda <dmagda@gridgain.com>
Authored: Fri Sep 15 09:59:24 2017 -0700
Committer: Denis Magda <dmagda@gridgain.com>
Committed: Fri Sep 15 09:59:24 2017 -0700

 README.md  | 172 --------------------------------------------------------
 README.txt |   1 +
 2 files changed, 1 insertion(+), 172 deletions(-)

diff --git a/README.md b/README.md
index d018878..a968ca1 100644
--- a/README.md
+++ b/README.md
@@ -46,178 +46,6 @@ With the Ignite Persistence enabled, you no longer need to keep all the
data and
-## Advanced Clustering
-Ignite nodes can automatically discover each other. This helps to scale the cluster when
needed, without having to restart the whole cluster. Developers can also leverage from Ignite’s
hybrid cloud support that allows establishing connection between private cloud and public
clouds such as Amazon Web Services, providing them with best of both worlds.
-<p align="center">
-    <a href="https://apacheignite.readme.io/docs/cluster">
-        <img src="https://ignite.apache.org/images/advanced-clustering.png" />
-    </a>
-Apache Ignite can be deployed on:
-* AWS
-* Docker
-* Google Cloud
-* Kubernetes
-* Mesos
-## Data Grid (JCache)
-Ignite data grid is an in-memory distributed key-value store which can be viewed as a distributed
partitioned hash map, with every cluster node owning a portion of the overall data. This way
the more cluster nodes we add, the more data we can cache.
-Unlike other key-value stores, Ignite determines data locality using a pluggable hashing
algorithm. Every client can determine which node a key belongs to by plugging it into a hashing
function, without a need for any special mapping servers or name nodes.
-Ignite data grid supports local, replicated, and partitioned data sets and allows to freely
cross query between these data sets using standard SQL syntax. Ignite supports standard SQL
for querying in-memory data including support for distributed SQL joins.
-<p align="center">
-    <a href="https://apacheignite.readme.io/docs/data-grid">
-        <img src="https://ignite.apache.org/images/in-memory-data-grid.jpg" />
-    </a>
-Our data grid offers many features, some of which are:
-* Primary & Backup Copies
-* Near Caches
-* Cache queries and SQL queries
-* Continuous Queries
-* Transactions
-* Off-Heap Memory
-* Affinity Collocation
-* Persistent Store
-* Automatic Persistence
-* Data Loading
-* Eviction and Expiry Policies
-* Data Rebalancing
-* Web Session Clustering
-* Hibernate L2 Cache
-* JDBC Driver
-* Spring Caching
-* Topology Validation
-## SQL Grid
-Apache Ignite incorporates distributed SQL database capabilities as a part of its platform.
The database is horizontally scalable, fault tolerant and SQL ANSI-99 compliant. It supports
all SQL, DDL, and DML commands including SELECT, UPDATE, INSERT, MERGE, and DELETE queries.
It also provides support for a subset of DDL commands relevant for distributed databases.
-With Ignite Durable Memory architecture, data as well as indexes can be stored both in memory
and, optionally, on disk. This allows executing distributed SQL operations across different
memory layers achieving in-memory performance with the durability of disk.
-You can interact with Apache Ignite using the SQL language via natively developed APIs for
Java, .NET and C++, or via the Ignite JDBC or ODBC drivers. This provides a true cross-platform
connectivity from languages such as PHP, Ruby and more.
-<p align="center">
-    <a href="https://apacheignite.readme.io/docs/distributed-sql">
-        <img src="https://ignite.apache.org/images/ignite-distributed-database.png" vspace="15"
-    </a>
-## Compute Grid
-Distributed computations are performed in parallel fashion to gain high performance, low
latency, and linear scalability. Ignite compute grid provides a set of simple APIs that allow
users distribute computations and data processing across multiple computers in the cluster.
Distributed parallel processing is based on the ability to take any computation and execute
it on any set of cluster nodes and return the results back.
-<p align="center">
-    <a href="https://apacheignite.readme.io/docs/compute-grid">
-        <img src="https://ignite.apache.org/images/in_memory_compute.png" vspace="15"/>
-    </a>
-We support these features, amongst others:
-* Distributed Closure Execution
-* MapReduce & ForkJoin Processing
-* Continuous Mapping
-* Clustered Executor Service
-* Per-Node Shared State
-* Collocation of Compute and Data
-* Load Balancing
-* Fault Tolerance
-* Job State Checkpointing
-* Job Scheduling
-## Service Grid
-Service Grid allows for deployments of arbitrary user-defined services on the cluster. You
can implement and deploy any service, such as custom counters, ID generators, hierarchical
maps, etc.
-Ignite allows you to control how many instances of your service should be deployed on each
cluster node and will automatically ensure proper deployment and fault tolerance of all the
-<p align="center">
-    <a href="https://apacheignite.readme.io/docs/service-grid">
-        <img src="https://ignite.apache.org/images/ignite_service.png" vspace="15"/>
-    </a>
-## Ignite File System
-Ignite File System (IGFS) is an in-memory file system allowing work with files and directories
over existing cache infrastructure. IGFS can either work as purely in-memory file system,
or delegate to another file system (e.g. various Hadoop file system implementations) acting
as a caching layer.
-In addition, IGFS provides API to execute map-reduce tasks over file system data.
-## Distributed Data Structures
-Ignite supports complex data structures in a distributed fashion:
-* Queues and sets: ordinary, bounded, collocated, non-collocated
-* Atomic types: `AtomicLong` and `AtomicReference`
-* `CountDownLatch`
-* ID Generators
-## Distributed Messaging
-Distributed messaging allows for topic based cluster-wide communication between all nodes.
Messages with a specified message topic can be distributed to all or sub-group of nodes that
have subscribed to that topic.
-Ignite messaging is based on publish-subscribe paradigm where publishers and subscribers
are connected together by a common topic. When one of the nodes sends a message A for topic
T, it is published on all nodes that have subscribed to T.
-## Distributed Events
-Distributed events allow applications to receive notifications when a variety of events occur
in the distributed grid environment. You can automatically get notified for task executions,
read, write or query operations occurring on local or remote nodes within the cluster.
-## Hadoop Accelerator
-Our Hadoop Accelerator provides a set of components allowing for in-memory Hadoop job execution
and file system operations.
-### MapReduce
-An alternate high-performant implementation of job tracker which replaces standard Hadoop
MapReduce. Use it to boost your Hadoop MapReduce job execution performance.
-<p align="center">
-    <a href="https://apacheignite-fs.readme.io/docs/map-reduce">
-        <img src="https://ignite.apache.org/images/hadoop-mapreduce.png" vspace="15" height="400"/>
-    </a>
-### IGFS - In-Memory File System
-A Hadoop-compliant IGFS File System implementation over which Hadoop can run over in plug-n-play
fashion and significantly reduce I/O and improve both, latency and throughput.
-<p align="center">
-    <a href="https://apacheignite-fs.readme.io/docs/in-memory-file-system">
-        <img src="https://ignite.apache.org/images/ignite_filesystem.png" height="300"
-    </a>
-### Secondary File System
-An implementation of `SecondaryFileSystem`. This implementation can be injected into existing
IGFS allowing for read-through and write-through behavior over any other Hadoop FileSystem
implementation (e.g. HDFS). Use it if you want your IGFS to become an in-memory caching layer
over disk-based HDFS or any other Hadoop-compliant file system.
-### Supported Hadoop distributions
-* Apache Hadoop
-* Cloudera CDH
-* Hortonworks HDP
-* Apache BigTop
-## Spark Shared RDDs
-Apache Ignite provides an implementation of Spark RDD abstraction which allows to easily
share state in memory across Spark jobs. The main difference between native Spark `RDD` and
`IgniteRDD` is that Ignite RDD provides a shared in-memory view on data across different Spark
jobs, workers, or applications, while native Spark RDD cannot be seen by other Spark jobs
or applications.
-<p align="center">
-    <a href="https://apacheignite-fs.readme.io/docs/ignite-for-spark">
-        <img src="https://ignite.apache.org/images/spark-ignite-rdd.png" height="400"
vspace="15" />
-    </a>
 ## Ignite Facts
 <b>Is Ignite a persistent or pure in-memory storage?</b><br/>

diff --git a/README.txt b/README.txt
index 9133f2c..9d91e01 100644
--- a/README.txt
+++ b/README.txt
@@ -8,6 +8,7 @@ The main feature set of Ignite In-Memory Data Fabric includes:
 * Advanced Clustering
 * Compute Grid
 * Data Grid
+* Distributed SQL
 * Service Grid
 * IGFS - Ignite File System
 * Distributed Data Structures

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