### 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 the ability to add a context to internally generated druid broker queries
* fix docs
* changes after first CI failure
* cleanup after merge with master
* change default to empty map and improve unit tests
* add doc info and fix checkstyle
* refactor DruidSchema#runSegmentMetadataQuery and add a unit test
The new config is an extension of the concept of "watchedTiers" where
the Broker can choose to add the info of only the specified tiers to its timeline.
Similarly, with this config, Broker can choose to ignore the segments being served
by the specified historical tiers. By default, no tier is ignored.
This config is useful when you want a completely isolated tier amongst many other tiers.
Say there are several tiers of historicals Tier T1, Tier T2 ... Tier Tn
and there are several brokers Broker B1, Broker B2 .... Broker Bm
If we want only Broker B1 to query Tier T1, instead of setting a long list of watchedTiers
on each of the other Brokers B2 ... Bm, we could just set druid.broker.segment.ignoredTiers=["T1"]
for these Brokers, while Broker B1 could have druid.broker.segment.watchedTiers=["T1"]
* Support real query cancelling for web console
* use uuid for queryId, create isSql reuse variable, and add catch for rejectionhandled promise
* remove delete api promise.then() response
* slove conflicts
* update read me with debug
* add degub code to test why CI failed
* included a druid extension called druid-testing-tools and it is not build nor loaded by default
* remove unuse variable
* remove debug log
* update docs with X-Druid-SQL-Query-Id
* review comments
* update header description
* Update docs/querying/sql.md
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* Update docs/querying/sql.md
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* Rename field, fix router documentation
* Add more lines to doc
* Apply doc suggestions from code review
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* Fix issue of duplicate key under certain conditions when loading late data in streaming. Also fixes a documentation issue with skipSegmentLineageCheck.
* maxId may be null at this point, need to check for that
* Remove hypothetical case (it cannot happen)
* Revert compaction is simply "killing" the compacted segment and previously, used, overshadowed segments are visible again
* Add comments
* Add handoff wait time to ingestion stats report. Refactor some code for batch handoff
* fix checkstyle
* Add assertion to AbstractITBatchIndexTask to make sure report reflects wait for segments happened
* add docs to the task reports section of doc
* refactor sql authorization to get resource type from schema, refactor resource type from enum to string
* information schema auth filtering adjustments
* refactor
* minor stuff
* Update SqlResourceCollectorShuttle.java
* Update sql.md
Added two example queries and adjusted formatting of one that was already there
* Update docs/querying/sql.md
Co-authored-by: Frank Chen <frankchen@apache.org>
* Update docs/querying/sql.md
Co-authored-by: Frank Chen <frankchen@apache.org>
* Update docs/querying/sql.md
Co-authored-by: Frank Chen <frankchen@apache.org>
* Update docs/querying/sql.md
Co-authored-by: Frank Chen <frankchen@apache.org>
* Update sql.md
Co-authored-by: Frank Chen <frankchen@apache.org>
When CommonCachedNotifier is being stopped while the thread is waiting on updateQueue.take(),
an InterruptedException is thrown. The stack trace from this exception gives the wrong idea that something went wrong with the shutdown.
* add MV_FILTER_ONLY SQL function, and list filter virtual column
* MV_FILTER_NONE and more tests
* formatting
* o yeah, forgot can do easy thing
* style
* hmm why was that there
* test filtering on virtual column
* style
* meh
* do it right
* good bot
The SQL "array" and "object" formats are intended to return invalid JSON
(lacking a ] terminator) if an error occurs midstream. This enables callers
to detect truncated responses. But JsonGenerators, by default, close JSON
arrays even when not explicitly told to.
This patch disables automatic array closing, which fixes the problem with
truncated response detection. It also adds tests for truncated responses
for all result formats.
* Update index.md
Moved H4s underneath the H3 for the task log location and added hyperlinks.
* Update tasks.md
Added process information around log file generation, and subsumed text from the configuration guide into this explanatory text instead.
* Update tasks.md
.html > .md
* Update docs/ingestion/tasks.md
Co-authored-by: Frank Chen <frankchen@apache.org>
Co-authored-by: Frank Chen <frankchen@apache.org>
* Update description of batchProcessingMode
Update the description to explicitly mention a released version of Druid that the original version was referencing
* Update docs/configuration/index.md
* Update docs/configuration/index.md
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* Update granularities.md
Link-back to the ingestion spec as well as Native queries plus examples.
* Update docs/querying/granularities.md
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* Update docs/querying/granularities.md
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
lokesh-lingarajan
Arun Ramani
Charles Smith
Lucas Capistrant
Kashif Faraz
Frank Chen
Vadim Ogievetsky
Clint Wylie
Maytas Monsereenusorn
andreacyc
Xavier Léauté
Victoria Lim
Caroline1000
Vaibhav
sthetland
Jihoon Son
Agustin Gonzalez
William Hyun
Peter Marshall
Jonathan Wei
Gian Merlino
Suneet Saldanha