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From sboi...@apache.org
Subject [15/26] ignite git commit: Updated Ignite description
Date Mon, 18 Sep 2017 08:43:15 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/1b5ca444
Tree: http://git-wip-us.apache.org/repos/asf/ignite/tree/1b5ca444
Diff: http://git-wip-us.apache.org/repos/asf/ignite/diff/1b5ca444

Branch: refs/heads/ignite-6149
Commit: 1b5ca4446f7961e6c1b3f9c6bab0d8dc540b932f
Parents: 8bcd69d
Author: Denis Magda <dmagda@gridgain.com>
Authored: Fri Sep 15 09:54:44 2017 -0700
Committer: Denis Magda <dmagda@gridgain.com>
Committed: Fri Sep 15 09:54:44 2017 -0700

----------------------------------------------------------------------
 README.md | 194 +++++++++++++++++++++++++++++++++++++++++----------------
 1 file changed, 140 insertions(+), 54 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/ignite/blob/1b5ca444/README.md
----------------------------------------------------------------------
diff --git a/README.md b/README.md
index b9833ef..d018878 100644
--- a/README.md
+++ b/README.md
@@ -1,20 +1,50 @@
-# Apache Ignite In-Memory Data Fabric
+# Apache Ignite In-Memory Computing Platform
 
-<img src="https://ignite.apache.org/images/logo3.png" hspace="20" />
+<a href="https://ignite.apache.org/"><img src="https://ignite.apache.org/images/logo3.png"
hspace="20"/></a>
 
 [![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)
 
 <a href="http://ci.ignite.apache.org/viewType.html?buildTypeId=IgniteTests_IgniteBasic&branch_IgniteTests=%3Cdefault%3E"><img
src="http://ci.ignite.apache.org/app/rest/builds/buildType:(id:IgniteTests_IgniteBasic)/statusIcon"
/></a>
 
-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.
+[Apache Ignite][apache-homepage] is the <b>in-memory</b> computing platform
+that is <b>durable</b>, <b>strongly consistent</b>, and <b>highly
available</b>
+with powerful <b>SQL</b>, <b>key-value</b> and <b>processing</b>
APIs.
 
 <p align="center">
     <a href="https://apacheignite.readme.io/docs">
-        <img src="https://ignite.apache.org/images/apache-ignite.png" />
+        <img src="https://ignite.apache.org/images/durable-memory.png" width="900px"/>
     </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).
+## Durable Memory
+Ignite's durable memory component treats RAM not just as a caching layer but as a complete
fully functional storage layer. This means that users can turn the persistence on and off
as needed. If the persistence is off, then Ignite can act as a distributed **in-memory database**
or **in-memory data grid**, depending on whether you prefer to use SQL or key-value APIs.
If the persistence is turned on, then Ignite becomes a distributed, **horizontally scalable
database** that guarantees full data consistency and is resilient to full cluster failures.
+
+## ACID Compliance
+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.
+
+## Complete SQL Support
+Ignite provides full support for SQL, DDL and DML, allowing users to interact with Ignite
using pure SQL without writing any code. This means that users can create tables and indexes
as well as insert, update, and query data using only SQL. Having such complete SQL support
makes Ignite a one-of-a-kind **distributed SQL database**.
+
+## Key-Value
+The in-memory data grid component in Ignite is a fully transactional **distributed key-value
store** that can scale horizontally across 100s of servers in the cluster. When persistence
is enabled, Ignite can also store more data than fits in memory and survive full cluster restarts.
+
+## Collocated Processing
+Most traditional databases work in a client-server fashion, meaning that data must be brought
to the client side for processing. This approach requires lots of data movement from servers
to clients and generally does not scale. Ignite, on the other hand, allows for sending light-weight
computations to the data, i.e. **collocating** computations with data. As a result, Ignite
scales better and minimizes data movement.
+
+## Scalability and Durability
+Ignite is an elastic, horizontally scalable distributed system that supports adding and removing
cluster nodes on demand. Ignite also allows for storing multiple copies of the data, making
it resilient to partial cluster failures. If the persistence is enabled, then data stored
in Ignite will also survive full cluster failures. Cluster restarts in Ignite can be very
fast, as the data becomes operational instantaneously directly from disk. As a result, the
data does not need to be preloaded in-memory to begin processing, and Ignite caches will lazily
warm up resuming the in memory performance.
+
+## Ignite Persistence
+
+Ignite Native Persistence is a distributed ACID and SQL-compliant disk store that transparently
integrates with Ignite's Durable Memory as an optional disk layer storing data and indexes
on SSD, Flash, 3D XPoint, and other types of non-volatile storages.
+
+With the Ignite Persistence enabled, you no longer need to keep all the data and indexes
in memory or warm it up after a node or cluster restart because the Durable Memory is tightly
coupled with persistence and treats it as a secondary memory tier. This implies that if a
subset of data or an index is missing in RAM, the Durable Memory will take it from the disk.
+
+<p align="center">
+    <a href="https://apacheignite.readme.io/docs/distributed-persistent-store">
+        <img src="https://ignite.apache.org/images/check_pointing-2.png" width="400px"/>
+    </a>
+</p>
 
 ## Advanced Clustering
 
@@ -26,6 +56,14 @@ Ignite nodes can automatically discover each other. This helps to scale
the clus
     </a>
 </p>
 
+Apache Ignite can be deployed on:
+* AWS
+* Docker
+* Google Cloud
+* Kubernetes
+* Mesos
+* YARN
+
 ## 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.
@@ -42,64 +80,61 @@ Ignite data grid supports local, replicated, and partitioned data sets
and allow
 
 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.
+* 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.
+* Web Session Clustering
+* Hibernate L2 Cache
+* JDBC Driver
+* Spring Caching
+* Topology Validation
 
-## Streaming & CEP
+## 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.
 
-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.
+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.
 
-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>
+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.
 
-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 href="https://apacheignite.readme.io/docs/distributed-sql">
+        <img src="https://ignite.apache.org/images/ignite-distributed-database.png" vspace="15"
width="400px"/>
     </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" />
+        <img src="https://ignite.apache.org/images/in_memory_compute.png" vspace="15"/>
     </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.
+* 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
 
@@ -121,12 +156,12 @@ 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: 
+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.
+* Queues and sets: ordinary, bounded, collocated, non-collocated
+* Atomic types: `AtomicLong` and `AtomicReference`
+* `CountDownLatch`
+* ID Generators
 
 ## Distributed Messaging
 
@@ -147,7 +182,7 @@ Our Hadoop Accelerator provides a set of components allowing for in-memory
Hadoo
 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">
+    <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>
 </p>
@@ -157,7 +192,7 @@ An alternate high-performant implementation of job tracker which replaces
standa
 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">
+    <a href="https://apacheignite-fs.readme.io/docs/in-memory-file-system">
         <img src="https://ignite.apache.org/images/ignite_filesystem.png" height="300"
vspace="15"/>
     </a>
 </p>
@@ -168,21 +203,72 @@ An implementation of `SecondaryFileSystem`. This implementation can
be injected
 
 ### Supported Hadoop distributions
 
-* Apache Hadoop.
-* Cloudera CDH.
-* Hortonworks HDP.
-* Apache BigTop.
+* 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">
+    <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>
 </p>
 
+## Ignite Facts
+
+<b>Is Ignite a persistent or pure in-memory storage?</b><br/>
+**Both**. Native persistence in Ignite can be turned on and off. This allows Ignite to store
data sets bigger than can fit in the available memory. Essentially, the smaller operational
data sets can be stored in-memory only, and larger data sets that do not fit in memory can
be stored on disk, using memory as a caching layer for better performance.
+
+[Read More](https://apacheignite.readme.io/docs/distributed-persistent-store)
+
+<b>Is Ignite a distributed database?</b><br/>
+**Yes**. Data in Ignite is either *partitioned* or *replicated* across a cluster of multiple
nodes. This provides scalability and adds resilience to the system. Ignite automatically controls
how data is partitioned, however, users can plug in their own distribution (affinity) functions
and collocate various pieces of data together for efficiency.
+
+[Read More](https://apacheignite.readme.io/docs/distributed-sql)
+
+<b>Is Ignite a relational SQL database?</b><br/>
+**Not fully**. Although Ignite aims to behave like any other relational SQL database, there
are differences in how Ignite handles constraints and indexes. Ignite supports *primary* and
*secondary* indexes, however, the *uniqueness* can only be enforced for the *primary* indexes.
Ignite also does not support *foreign key* constraints.
+
+Essentially, Ignite purposely does not support any constraints that would entail a cluster
broadcast message for each update and significantly hurt performance and scalability of the
system.
+
+[Read More](https://apacheignite.readme.io/docs/indexes)
+
+<b>Is Ignite an in-memory database?</b><br/>
+**Yes**. Even though Ignite *durable memory* works well in-memory and on-disk, the disk persistence
can be disabled and Ignite can act as a pure *in-memory database*.
+
+[Read More](https://apacheignite.readme.io/docs/distributed-sql)
+
+<b>Is Ignite a transactional database?</b><br/>
+**Not fully**. ACID Transactions are supported, but only at *key-value* API level. Ignite
also supports *cross-partition* transactions, which means that transactions can span keys
residing in different partitions on different servers.
+
+At *SQL* level Ignite supports *atomic*, but not yet *transactional* consistency. Ignite
community plans to implement SQL transactions in version 2.2.
+
+[Read More](https://apacheignite.readme.io/docs/sql-queries#known-limitations)
+
+<b>Is Ignite a key-value store?</b><br/>
+**Yes**. Ignite provides a feature rich key-value API, that is JCache (JSR-107) compliant
and supports Java, C++, and .NET.
+
+[Read More](https://apacheignite.readme.io/docs/data-grid)
+
+<b>Is Ignite an in-memory data grid?</b><br/>
+**Yes**. Ignite is a full-featured data grid, which can be used either in pure in-memory
mode or with Ignite native persistence. It can also integrate with any 3rd party database,
including any RDBMS or NoSQL store.
+
+[Read More](https://apacheignite.readme.io/docs/data-grid)
+
+<b>What is durable memory?</b><br/>
+Ignite *durable memory* architecture allows Ignite to extend in-memory computing to disk.
It is based on a paged-based off-heap memory allocator which becomes durable by persisting
to the *write-ahead-log (WAL)* and, then, to main Ignite persistent storage. When persistence
is disabled, durable memory acts like a pure in-memory storage.
+
+[Read More](https://apacheignite.readme.io/docs/durable-memory)
+
+<b>What is collocated processing?</b><br/>
+Ignite is a distributed system and, therefore, it is important to be able to collocate data
with data and compute with data to avoid distributed data noise. Data collocation becomes
especially important when performing distributed SQL joins. Ignite also supports sending user
logic (functions, lambdas, etc.) directly to the nodes where the data resides and computing
on the data locally.
+
+[Read More](https://apacheignite.readme.io/docs/collocate-compute-and-data)
+
 ## Ignite On Other Platforms
 
 <a href="modules/platforms/dotnet">Ignite.NET</a>


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