Apoursam/spark kafka input #1272
Conversation
eldernewborn
force-pushed
the
apoursam/spark-kafka-input
branch
from
76abd13
to
3e423e0
Compare
eldernewborn
force-pushed
the
apoursam/spark-kafka-input
branch
from
2fce71a
to
12f1777
Compare
eldernewborn
force-pushed
the
apoursam/spark-kafka-input
branch
from
ffa9689
to
060ec68
Compare
…s deeply entrenched in the system, adopting this approach for KIF repush. formatting fixes / datatype fixes / spotbug fixes
…merBugger.get that comes back as null .
…issing records situations.
eldernewborn
force-pushed
the
apoursam/spark-kafka-input
branch
from
060ec68
to
2bc56ee
Compare
…. Polished logging.
| @@ -20,6 +20,16 @@ public class SparkConstants { | |||
| new StructField[] { new StructField(KEY_COLUMN_NAME, BinaryType, false, Metadata.empty()), | |||
| new StructField(VALUE_COLUMN_NAME, BinaryType, true, Metadata.empty()) }); | |||
|
|
|||
| public static final StructType KAFKA_INPUT_TABLE_SCHEMA = new StructType( | |||
There was a problem hiding this comment.
Would we need to version this at all?
There was a problem hiding this comment.
My plan is to keep this as is for the most part ( after getting a few runs in EI ) . and for the consistency checker I'll have an extended form of the table schema that contains the offset vectors and all the goodies prebaked and present for consistency checker.
If there are any specifics that you think we need for this ( and the ETL functionality ) Please advise.
There was a problem hiding this comment.
I guess what I'm wondering, do we need to be concerned if this struct type changes.... my initial thinking is that it's probably ok since this used to generate NEW datasets, not process old ones?
|
|
||
|
|
||
| public class PartitionSplitters { | ||
| // need a method called fullPartitionSplitter, takes in list of partition start and end offsets |
There was a problem hiding this comment.
Why not use proper javadoc style comments?
| public class PartitionSplitters { | ||
| // need a method called fullPartitionSplitter, takes in list of partition start and end offsets | ||
| // and returns a list of VenicePubsubInputPartition splits | ||
| public static Map<Integer, List<List<Long>>> fullPartitionSplitter(Map<Integer, List<Long>> partitionOffsetsMap) { |
There was a problem hiding this comment.
It looks like theres an assumption that the list in entries for Map<Integer, List> partitionOffsetsMap need to be two entries, a start and an end. I'm not sure I see yet in this PR how this method is used, but it looks like between this and assembleSegment, an interface which uses a Pair might make more sense?
Or is there a flexibility gain in using a List that I'm missing?
| return segment; | ||
| } | ||
|
|
||
| static long computeIntendedSplitLengthBasedOnCount(Map<Integer, List<Long>> partitionOffsetsMap, int totalSegments) { |
There was a problem hiding this comment.
What is the semantic difference between offsets and segments?
| properties.putAll(configs); | ||
| // the properties here is the entry point for all the configurations | ||
| // we receive from the outer layer. | ||
| // schem and partitioning are useless and should be discarded? |
There was a problem hiding this comment.
I think these were notes?
| Properties properties = jobConfig.getPropertiesCopy(); | ||
| properties.putAll(options.asCaseSensitiveMap()); | ||
|
|
||
| return new VenicePubsubInputScanBuilder(new VeniceProperties(properties)); // should we flip this to |
There was a problem hiding this comment.
It looks like this comment isn't necessary?
| PubSubTopicRepository pubSubTopicRepository = new PubSubTopicRepository(); | ||
| PubSubTopic pubSubTopic = pubSubTopicRepository.getTopic(topicName); | ||
| PubSubClientsFactory clientsFactory = new PubSubClientsFactory(jobConfig); | ||
| // PubSubAdminAdapter pubsubAdminClient = |
[vpj] Spark input module to read from a Kafka topic and populate a dataframe
Introduced the VenicePubsubSource which is a table provider, backed by Kafka topics to materialize contents of a Venice Kafka topic as a dataframe to be used by Spark Jobs.
This is a foundational building block for KIF functionality (repush data from a Kafka topic source) as well as Data consistency Checker.
The implementation offers some niceties like a splitter that can split the task into chunks for better parallelism, which does so considering the available start and end offsets in topics, to account for message TTL and compaction.
Future Work to be done:
Add support for chunked messages and compressed messages.
How was this PR tested?
unit tests, integration tests, manual testing.
Does this PR introduce any user-facing changes?