* add data format and example for featureSpec
* add second feature in example
* Apply suggestions from code review
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* Add jsonPath functions support
* Add jsonPath function test for Avro
* Add jsonPath function length() to Orc
* Add jsonPath function length() to Parquet
* Add more tests to ORC format
* update doc
* Fix exception during ingestion
* Add IT test case
* Revert "Fix exception during ingestion"
This reverts commit 5a5484b9ea.
* update IT test case
* Add 'keys()'
* Commit IT test case
* Fix UT
* Corrected admonition issue
* Update data-formats.md
Removed all admonition bits, and took out sf linebreaks.
* Update data-formats.md
Changed the shocker line into something a little more practical.
### Description
Today we ingest a number of high cardinality metrics into Druid across dimensions. These metrics are rolled up on a per minute basis, and are very useful when looking at metrics on a partition or client basis. Events is another class of data that provides useful information about a particular incident/scenario inside a Kafka cluster. Events themselves are carried inside kafka payload, but nonetheless there are some very useful metadata that is carried in kafka headers that can serve as useful dimension for aggregation and in turn bringing better insights.
PR(https://github.com/apache/druid/pull/10730) introduced support of Kafka headers in InputFormats.
We still need an input format to parse out the headers and translate those into relevant columns in Druid. Until that’s implemented, none of the information available in the Kafka message headers would be exposed. So first there is a need to write an input format that can parse headers in any given format(provided we support the format) like we parse payloads today. Apart from headers there is also some useful information present in the key portion of the kafka record. We also need a way to expose the data present in the key as druid columns. We need a generic way to express at configuration time what attributes from headers, key and payload need to be ingested into druid. We need to keep the design generic enough so that users can specify different parsers for headers, key and payload.
This PR is designed to solve the above by providing wrapper around any existing input formats and merging the data into a single unified Druid row.
Lets look at a sample input format from the above discussion
"inputFormat":
{
"type": "kafka", // New input format type
"headerLabelPrefix": "kafka.header.", // Label prefix for header columns, this will avoid collusions while merging columns
"recordTimestampLabelPrefix": "kafka.", // Kafka record's timestamp is made available in case payload does not carry timestamp
"headerFormat": // Header parser specifying that values are of type string
{
"type": "string"
},
"valueFormat": // Value parser from json parsing
{
"type": "json",
"flattenSpec": {
"useFieldDiscovery": true,
"fields": [...]
}
},
"keyFormat": // Key parser also from json parsing
{
"type": "json"
}
}
Since we have independent sections for header, key and payload, it will enable parsing each section with its own parser, eg., headers coming in as string and payload as json.
KafkaInputFormat will be the uber class extending inputFormat interface and will be responsible for creating individual parsers for header, key and payload, blend the data resolving conflicts in columns and generating a single unified InputRow for Druid ingestion.
"headerFormat" will allow users to plug parser type for the header values and will add default header prefix as "kafka.header."(can be overridden) for attributes to avoid collision while merging attributes with payload.
Kafka payload parser will be responsible for parsing the Value portion of the Kafka record. This is where most of the data will come from and we should be able to plugin existing parser. One thing to note here is that if batching is performed, then the code is augmenting header and key values to every record in the batch.
Kafka key parser will handle parsing Key portion of the Kafka record and will ingest the Key with dimension name as "kafka.key".
## KafkaInputFormat Class:
This is the class that orchestrates sending the consumerRecord to each parser, retrieve rows, merge the columns into one final row for Druid consumption. KafkaInputformat should make sure to release the resources that gets allocated as a part of reader in CloseableIterator<InputRow> during normal and exception cases.
During conflicts in dimension/metrics names, the code will prefer dimension names from payload and ignore the dimension either from headers/key. This is done so that existing input formats can be easily migrated to this new format without worrying about losing information.
* add protobuf inputformat
* repair pom
* alter intermediateRow to type of Dynamicmessage
* add document
* refine test
* fix document
* add protoBytesDecoder
* refine document and add ser test
* add hash
* add schema registry ser test
Co-authored-by: yuanyi <yuanyi@freewheel.tv>
* Add config and header support for confluent schema registry. (porting code from https://github.com/apache/druid/pull/9096)
* Add Eclipse Public License 2.0 to license check
* Update licenses.yaml, revert changes to check-licenses.py and dependencies for integration-tests
* Add spelling exception and remove unused dependency
* Use non-deprecated getSchemaById() and remove duplicated license entry
* Update docs/ingestion/data-formats.md
Co-authored-by: Clint Wylie <cjwylie@gmail.com>
* Added check for schema being null, as per Confluent code
* Missing imports and whitespace
* Updated unit tests with AvroSchema
Co-authored-by: Sergio Spinatelli <sergio.spinatelli.extern@7-tv.de>
Co-authored-by: Sergio Spinatelli <sergio.spinatelli.extern@joyn.de>
Co-authored-by: Clint Wylie <cjwylie@gmail.com>
* Fix Avro OCF detection prefix and run formation detection on raw input
* Support Avro Fixed and Enum types correctly
* Check Avro version byte in format detection
* Add test for AvroOCFReader.sample
Ensures that the Sampler doesn't receive raw input that it can't
serialize into JSON.
* Document Avro type handling
* Add TS unit tests for guessInputFormat
* Add AvroOCFInputFormat
* Support supplying a reader schema in AvroOCFInputFormat
* Add docs for Avro OCF input format
* Address review comments
* Address second round of review
* Update data-formats.md
Per Suneet, "Since you're editing this file can you also fix the json on line 177 please - it's missing a comma after the }"
* Light text cleanup
* Removing discussion of sample data, since it's repeated in the data loading tutorial, and not immediately relevant here.
* Update index.md
* original quickstart full first pass
* original quickstart full first pass
* first pass all the way through
* straggler
* image touchups and finished old tutorial
* a bit of finishing up
* Review comments
* fixing links
* spell checking gymnastics
* Update data-formats.md
Field error and light rewording of new Avro material (and working through the doc authoring process).
* Update data-formats.md
Make default statements consistent. Future change: s/=/is.
* Doc update for new input source and input format.
- The input source and input format are promoted in all docs under docs/ingestion
- All input sources including core extension ones are located in docs/ingestion/native-batch.md
- All input formats and parsers including core extension ones are localted in docs/ingestion/data-formats.md
- New behavior of the parallel task with different partitionsSpecs are documented in docs/ingestion/native-batch.md
* parquet
* add warning for range partitioning with sequential mode
* hdfs + s3, gs
* add fs impl for gs
* address comments
* address comments
* gcs
Victoria Lim
Jonathan Wei
Frank Chen
Peter Marshall
Charles Smith
lokesh-lingarajan
Yi Yuan
sthetland
spinatelli
Joseph Glanville
LightGHLi
Atul Mohan
Jihoon Son
Gian Merlino