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From voze...@apache.org
Subject [08/50] [abbrv] ignite git commit: Add a README.md.
Date Tue, 29 Dec 2015 10:26:05 GMT
Add a README.md.


Project: http://git-wip-us.apache.org/repos/asf/ignite/repo
Commit: http://git-wip-us.apache.org/repos/asf/ignite/commit/4d1cc2b8
Tree: http://git-wip-us.apache.org/repos/asf/ignite/tree/4d1cc2b8
Diff: http://git-wip-us.apache.org/repos/asf/ignite/diff/4d1cc2b8

Branch: refs/heads/ignite-2265
Commit: 4d1cc2b82d7bc21a9981a0b83c771c09e645c62c
Parents: 03ceffa
Author: Raul Kripalani <raulk@apache.org>
Authored: Wed Dec 2 15:02:18 2015 +0000
Committer: Raul Kripalani <raulk@apache.org>
Committed: Wed Dec 2 15:02:18 2015 +0000

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 README.md | 194 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 194 insertions(+)
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http://git-wip-us.apache.org/repos/asf/ignite/blob/4d1cc2b8/README.md
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diff --git a/README.md b/README.md
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+# Apache Ignite In-Memory Data Fabric
+
+<img src="https://ignite.apache.org/images/logo2.png" hspace="20" />
+
+[![Join the chat at https://gitter.im/apacheignite/ignite](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/apacheignite/ignite?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+
+The [Apache Ignite][apache-homepage] In-Memory Data Fabric is a high-performance, integrated
and distributed in-memory platform for computing and transacting on large-scale data sets
in real-time, orders of magnitude faster than possible with traditional disk-based or flash
technologies.
+
+<p align="center">
+    <a href="https://apacheignite.readme.io/docs">
+        <img src="https://ignite.apache.org/images/apache-ignite.png" />
+    </a>
+</p>
+
+Apache Ignite In-Memory Data Fabric is designed to deliver uncompromised performance for
a wide set of in-memory computing use cases from [high performance computing](https://ignite.apache.org/features.html),
to the industry most advanced [data grid](https://ignite.apache.org/features.html), highly
available [service grid](https://ignite.apache.org/features.html), and [streaming](https://ignite.apache.org/features.html).
+
+## 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>
+</p>
+
+## 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>
+</p>
+
+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.
+
+## Streaming & CEP
+
+Ignite streaming allows to process continuous never-ending streams of data in scalable and
fault-tolerant fashion. The rates at which data can be injected into Ignite can be very high
and easily exceed millions of events per second on a moderately sized cluster.
+
+Real-time data is ingested via data streamers. We offer streamers for JMS 1.1, Apache Kafka,
MQTT, Twitter, Apache Flume and Apache Camel already, and we keep adding new ones every release.
+
+<p align="center">
+    <a href="https://apacheignite.readme.io/docs/streaming--cep">
+        <img src="https://ignite.apache.org/images/data-streamers.png" />
+    </a>
+</p>
+
+The data can then be queried within sliding windows, if needed:
+
+<p align="center">
+    <a href="https://apacheignite.readme.io/docs/streaming--cep">
+        <img src="https://ignite.apache.org/images/sliding-event-window.png" />
+    </a>
+</p>
+
+## 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" />
+    </a>
+</p>
+
+We support these features, amongst others:
+
+* Distributed Closure Execution.
+* MapReduce & ForkJoin Processing.
+* Clustered Executor Service.
+* 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
services.
+
+<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>
+</p>
+
+## 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.readme.io/docs/map-reduce">
+        <img src="https://ignite.apache.org/images/hadoop-mapreduce.png" vspace="15" height="400"/>
+    </a>
+</p>
+
+### 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.readme.io/docs/file-system">
+        <img src="https://ignite.apache.org/images/ignite_filesystem.png" height="300"
vspace="15"/>
+    </a>
+</p>
+
+### 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.readme.io/docs/shared-rdd">
+        <img src="https://ignite.apache.org/images/spark-ignite-rdd.png" height="400"
vspace="15" />
+    </a>
+</p>
+
+## Getting Started
+
+For information on how to get started with Apache Ignite please visit: [Getting Started][getting-started].
+
+## Full Documentation
+
+You can find the full Apache Ignite documentation here: [Full documentation][docs].
+
+[apache-homepage]: https://ignite.apache.org/
+[getting-started]: https://apacheignite.readme.io/docs/getting-started
+[docs]: https://apacheignite.readme.io/docs
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