Stuart Macdonald created IGNITE-9108:
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Summary: Spark DataFrames With Cache Key and Value Objects
Key: IGNITE-9108
URL: https://issues.apache.org/jira/browse/IGNITE-9108
Project: Ignite
Issue Type: New Feature
Components: spark
Reporter: Stuart Macdonald
Add support for _key and _val columns within Ignite-provided Spark DataFrames,
which represent the cache key and value objects similar to the current
_key/_val column semantics in Ignite SQL.
If the cache key or value objects are standard SQL types (eg. String, Int, etc)
they will be represented as such in the DataFrame schema, otherwise they are
represented as Binary types encoded as either: 1. Ignite BinaryObjects, in
which case we'd need to supply a Spark Encoder implementation for
BinaryObjects, eg:
{code:java}
IgniteSparkSession session = ...
Dataset<Row> dataFrame = ...
Dataset<MyValClass> valDataSet =
dataFrame.select("_val_).as(session.binaryObjectEncoder(MyValClass.class))
{code}
Or 2. Kryo-serialised versions of the objects, eg:
{code:java}
Dataset<Row> dataFrame = ...
DataSet<MyValClass> dataSet =
dataFrame.select("_val_).as(Encoders.kryo(MyValClass.class))
{code}
Option 1 would probably be more efficient but option 2 would be more idiomatic
Spark.
The rationale behind this is the same as the Ignite SQL _key and _val columns:
to allow access to the full cache objects from a SQL context.
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