IgniteContext and IgniteRDD
IgniteContext is the main entry point to Spark-Ignite integration. To create an instance of Ignite context, user must provide an instance of SparkContext and a closure creating
IgniteConfiguration (configuration factory). Ignite context will make sure that server or client Ignite nodes exist in all involved job instances. Alternatively, a path to an XML configuration file can be passed to
IgniteContext constructor which will be used to configure nodes being started.
When creating an
IgniteContext instance, an optional boolean
client argument (defaulting to
true) can be passed to context constructor. This is typically used in a Shared Deployment installation. When
client is set to
false, context will operate in embedded mode and will start server nodes on all workers during the context construction. This is required in an Embedded Deployment installation. See Installation for information on deployment configurations.
Embedded Mode Deprecation
Embedded mode implies starting Ignite server nodes within Spark executors which can cause unexpected rebalancing or even data loss. Therefore this mode is currently deprecated and will be eventually discontinued. Consider starting a separate Ignite cluster and using standalone mode to avoid data consistency and performance issues.
IgniteContext is created, instances of
IgniteRDD may be obtained using
fromCache methods. It is not required that requested cache exist in Ignite cluster when RDD is created. If the cache with the given name does not exist, it will be created using provided configuration or template configuration.
For example, the following code will create an Ignite context with default Ignite configuration
val igniteContext = new IgniteContext(sparkContext, () => new IgniteConfiguration())
The following code will create an Ignite context configured from a file
val igniteContext = new IgniteContext(sparkContext, "examples/config/spark/example-shared-rdd.xml")
IgniteRDD is an implementation of Spark RDD abstraction representing a live view of Ignite cache.
IgniteRDD is not immutable, all changes in Ignite cache (regardless whether they were caused by another RDD or external changes in cache) will be visible to RDD users immediately.
IgniteRDD utilizes partitioned nature of Ignite caches and provides partitioning information to Spark executor. Number of partitions in
IgniteRDD equals to the number of partitions in underlying Ignite cache.
IgniteRDD also provides affinity information to Spark via
getPrefferredLocations method so that RDD computations use data locality.
Reading values from Ignite
IgniteRDD is a live view of Ignite cache, there is no need to explicitly load data to Spark application from Ignite. All RDD methods are available to use right away after an instance of
IgniteRDD is created.
For example, assuming an Ignite cache with name "partitioned" contains string values, the following code will find all values that contain the word "Ignite":
val cache = igniteContext.fromCache("partitioned") val result = cache.filter(_._2.contains("Ignite")).collect()
Saving values to Ignite
Since Ignite caches operate on key-value pairs, the most straightforward way to save values to Ignite cache is to use a Spark tuple RDD and
savePairs method. This method will take advantage of the RDD partitioning and store value to cache in a parallel manner, if possible.
It is also possible to save value-only RDD into Ignite cache using
saveValues method. In this case
IgniteRDD will generate a unique affinity-local key for each value being stored into the cache.
For example, the following code will store pairs of integers from 1 to 10000 into cache named "partitioned" using 10 parallel store operations:
val cacheRdd = igniteContext.fromCache("partitioned") cacheRdd.savePairs(sparkContext.parallelize(1 to 10000, 10).map(i => (i, i)))
Running SQL queries against Ignite cache
When Ignite cache is configured with the indexing subsystem enabled, it is possible to run SQL queries against the cache using
sql methods. See Working with SQL for more information about Ignite SQL queries.
For example, assuming the "partitioned" cache is configured to index pairs of integers, the following code will get all integers in the range (10, 100):
val cacheRdd = igniteContext.fromCache("partitioned") val result = cacheRdd.sql("select _val from Integer where val > ? and val < ?", 10, 100)
There are a couple of examples available on GitHub that demonstrate the usage of
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