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| <section id="memory-centric" class="page-section"> |
| <h1 class="first">What is Collocated Processing?</h1> |
| <div class="col-sm-12 col-md-12 col-xs-12" style="padding:0 0 20px 0;"> |
| <div class="col-sm-6 col-md-6 col-xs-12" style="padding-left:0; padding-right:0"> |
| <p> |
| The disk-centric systems, like RDBMS or NoSQL, generally utilize the classic client-server |
| approach, where the data is brought from the server to the client side where it gets processed |
| and then is usually discarded. This approach does not scale well as moving the data over the |
| network is the most expensive operation in a distributed system. |
| </p> |
| <p> |
| A much more scalable approach is <code>collocated</code> processing that reverses the flow by bringing |
| the computations to the servers where the data actually resides. This approach allows you to |
| execute advanced logic or distributed SQL with JOINs exactly where the data is stored avoiding |
| expensive serialization and network trips. |
| </p> |
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| </div> |
| </div> |
| |
| <div class="page-heading">Usage Example</div> |
| <p> |
| Let's assume that a blizzard is approaching New York. As a telecommunication company, you have to |
| send a warning text message to 8 million New Yorkers. |
| With the client-server approach the company has to move all <nobr>8 million (!)</nobr> records |
| from the database to the client text messaging application, which does not scale. |
| </p> |
| <p> |
| A much more efficient approach would be to send the text-messaging logic to the cluster node responsible |
| for storing the New York residents. This approach moves only 1 computation instead of 8 million records |
| across the network, and performs a lot better. |
| </p> |
| |
| <p> |
| Here is an example of how this logic might look like: |
| </p> |
| <!--<ul id="compute-example" class="nav nav-tabs">--> |
| <!--<li class="active"><a href="#compute-task" aria-controls="profile" data-toggle="tab">Message Broadcasting Logic</a></li>--> |
| <!--</ul>--> |
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| <div class="tab-content"> |
| |
| <div class="tab-pane active" id="compute-task"> |
| <pre class="brush:java"> |
| |
| Ignite ignite = ... |
| |
| // NewYork ID. |
| long newYorkId = 2; |
| |
| // Sending the logic to a cluster node that stores NewYork and all its inhabitants. |
| ignite.compute().affinityRun("City", newYorkId, new IgniteRunnable() { |
| |
| @IgniteInstanceResource |
| Ignite ignite; |
| |
| @Override |
| public void run() { |
| // Getting an access to Persons cache. |
| IgniteCache<BinaryObject, BinaryObject> people = ignite.cache("Person").withKeepBinary(); |
| |
| |
| ScanQuery<BinaryObject, BinaryObject> query = new ScanQuery <BinaryObject, BinaryObject>(); |
| |
| try (QueryCursor<Cache.Entry<BinaryObject, BinaryObject>> cursor = people.query(query)) { |
| // Iteration over the local cluster node data using the scan query. |
| for (Cache.Entry<BinaryObject, BinaryObject> entry : cursor) { |
| BinaryObject personKey = entry.getKey(); |
| |
| // Picking NewYorker's only. |
| if (personKey.<Long>field("CITY_ID") == newYorkId) { |
| person = entry.getValue(); |
| |
| // Sending the warning message to the person. |
| } |
| } |
| } |
| } |
| } |
| |
| </pre> |
| </div> |
| </div> |
| <p> |
| Check more <a href="https://apacheignite.readme.io/docs/affinity-collocation" target="_blank">here</a>. |
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