* latest datasketches-java and datasketches-memory
* updated versions of datasketches-java and datasketches-memory
Co-authored-by: AlexanderSaydakov <AlexanderSaydakov@users.noreply.github.com>
* better type system
* needle in a haystack
* ColumnCapabilities is a TypeSignature instead of having one, INFORMATION_SCHEMA support
* fixup merge
* more test
* fixup
* intern
* fix
* oops
* oops again
* ...
* more test coverage
* fix error message
* adjust interning, more javadocs
* oops
* more docs more better
this change ensures that JettyTest is setting the properties it needs in case some other test overwrites them
this also changes up the ordering of the call for setProperties to call super's first in case super is setting the same property
* Increment retry count to add more time for tests to pass
* Re-enable ITHttpInputSourceTest
* Restore original count
* This test is about input source, hash partitioning takes longer and not required thus changing to dynamic
* Further simplify by removing sketches
* Change ordering of config file vs env vars in Docker
Currently if you provide a config file it negates any settings set via environment variables.
This change allows use of a config file as a base and allow environment variables to override.
Additionally this allows dynamic features such as DRUID_SET_HOST to function correctly when a config file has been provided.
* Custom JAVA_OPTS should override service jvm.config
Follow up PR for #11680
Description
Supervisor and Task APIs are related to ingestion and must always require Datasource WRITE
authorization even if they are purely informative.
Changes
Check Datasource WRITE in SystemSchema for tables "supervisors" and "tasks"
Check Datasource WRITE for APIs /supervisor/history and /supervisor/{id}/history
Check Datasource for all Indexing Task APIs
### 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
Alexander Saydakov
David Ferlay
Clint Wylie
Sandeep
David Bar
TSFenwick
Charles Smith
Abhishek Agarwal
Agustin Gonzalez
andreacyc
Kashif Faraz
Arun Ramani
Victoria Lim
Joseph Glanville
Katya Macedo
lokesh-lingarajan
Lucas Capistrant
Frank Chen
Vadim Ogievetsky
Maytas Monsereenusorn
Xavier Léauté
Caroline1000
Vaibhav
sthetland
Jihoon Son
William Hyun