* refactor and link fixes
* add sql docs to left nav
* code format for needle
* updated web console script
* link fixes
* update earliest/latest functions
* edits for grammar and style
* more link fixes
* another link
* update with #12226
* update .spelling file
* Use Druid's extension loading for integration test instead of maven
* fix maven command
* override config path
* load input format extensions and kafka by default; add prepopulated-data group
* all docker-composes are overridable
* fix s3 configs
* override config for all
* fix docker_compose_args
* fix security tests
* turn off debug logs for overlord api calls
* clean up stuff
* revert docker-compose.yml
* fix override config for query error test; fix circular dependency in docker compose
* add back some dependencies in docker compose
* new maven profile for integration test
* example file filter
* Make nodeRole available during binding; add support for dynamic registration of DruidService
* fix checkstyle and test
* fix customRole test
* address comments
* add more javadoc
under "Aggregators", about the lgK setting, it said "Must be a power of 2 from 4 to 21 inclusively." 21 is not a power of 2, nor is 12, the given default. I think there may have been confusion because lgK represents log2 of K. We could say "K must be a power of 2...", or just say lgK must be between 4 and 21.
* Support routing data through an HTTP proxy
* Support routing data through an HTTP proxy
This adds the ability for the HttpClient to connect through an HTTP proxy. We
augment the channel factory to check if it is supposed to be proxied and, if so,
we connect to the proxy host first, issue a CONNECT command through to the final
recipient host and *then* give the channel to the normal http client for usage.
* add docs
* address comments
Co-authored-by: imply-cheddar <86940447+imply-cheddar@users.noreply.github.com>
Enhanced the ExtractionNamespace interface in lookups-cached-global core extension with the ability to set a maxHeapPercentage for the cache of the respective namespace. The reason for adding this functionality, is make it easier to detect when a lookup table grows to a size that the underlying service cannot handle, because it does not have enough memory. The default value of maxHeap for the interface is -1, which indicates that no maxHeapPercentage has been set. For the JdbcExtractionNamespace and UriExtractionNamespace implementations, the default value is null, which will cause the respective service that the lookup is loaded in, to warn when its cache is beyond mxHeapPercentage of the service's configured max heap size. If a positive non-null value is set for the namespace's maxHeapPercentage config, this value will be honored for all services that the respective lookup is loaded onto, and consequently log warning messages when the cache of the respective lookup grows beyond this respective percentage of the services configured max heap size. Warnings are logged every time that either Uri based or Jdbc based lookups are regenerated, if the maxHeapPercentage constraint is violated. No other implementations will log warnings at this time. No error is thrown when the size exceeds the maxHeapPercentage at this time, as doing so could break functionality for existing users. Previously the JdbcCacheGenerator generated its cache by materializing all rows of the underling table in memory at once; this made it difficult to log warning messages in the case that the results from the jdbc query were very large and caused the service to run out of memory. To help with this, this pr makes it so that the jdbc query results are instead streamed through an iterator.
Add support for hadoop 3 profiles . Most of the details are captured in #11791 .
We use a combination of maven profiles and resource filtering to achieve this. Hadoop2 is supported by default and a new maven profile with the name hadoop3 is created. This will allow the user to choose the profile which is best suited for the use case.
### 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.
* Configurable maxStreamLength for doubles sketches
* fix equals/hashcode and it test failure
* fix test
* fix it test
* benchmark
* doc
* grouping key
* fix comment
* dependency check
* Update docs/development/extensions-core/datasketches-quantiles.md
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* 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>
* 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>
* 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>
* Update docs/querying/sql.md
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* Add prometheus-emitter docs
* Update docs/development/extensions.md
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
This change updates doc to clarify when and how a change to druid.auth.authenticator.basic.credentialIterations takes effect: changes apply only to new users or existing users upon changing their password via the credentials API, which may not be the expectation.
* HLL lgK and a tip
Knowledge transfer from https://the-asf.slack.com/archives/CJ8D1JTB8/p1600699967024200. Attempted to make a connection between the SQL HLL function and the HLL underneath without getting too complicated. Also added a note about using K over 16 being pretty much pointless.
* Corrected spelling
* Create datasketches-hll.md
Put roll-up back to rollup
* Update docs/development/extensions-core/datasketches-hll.md
Co-authored-by: Abhishek Agarwal <1477457+abhishekagarwal87@users.noreply.github.com>
Co-authored-by: Abhishek Agarwal <1477457+abhishekagarwal87@users.noreply.github.com>
This PR splits current SegmentLoader into SegmentLoader and SegmentCacheManager.
SegmentLoader - this class is responsible for building the segment object but does not expose any methods for downloading, cache space management, etc. Default implementation delegates the download operations to SegmentCacheManager and only contains the logic for building segments once downloaded. . This class will be used in SegmentManager to construct Segment objects.
SegmentCacheManager - this class manages the segment cache on the local disk. It fetches the segment files to the local disk, can clean up the cache, and in the future, support reserve and release on cache space. [See https://github.com/Make SegmentLoader extensible and customizable #11398]. This class will be used in ingestion tasks such as compaction, re-indexing where segment files need to be downloaded locally.
* Avro union support
* Document new union support
* Add support for AvroStreamInputFormat and fix checkstyle
* Extend multi-member union test schema and format
* Some additional docs and add Enums to spelling
* Rename explodeUnions -> extractUnions
* explode -> extract
* ByType
* Correct spelling error
* allow user to set group.id for Kafka ingestion task
* fix test coverage by removing deprecated code and add doc
* fix typo
* Update docs/development/extensions-core/kafka-ingestion.md
Co-authored-by: frank chen <frankchen@apache.org>
Co-authored-by: frank chen <frankchen@apache.org>
* request logs through kafka emitter
* travis fixes
* review comments
* kafka emitter unit test
* new line
* travis checks
* checkstyle fix
* count request lost when request topic is null
Victoria Lim
Jihoon Son
jacobtolar
Charles Smith
benkrug
Maytas Monsereenusorn
zachjsh
Karan Kumar
lokesh-lingarajan
Frank Chen
Vaibhav
zhangyue19921010
Jeet Patel
Clint Wylie
sthetland
Yi Yuan
Peter Marshall
Paul Rogers
Abhishek Agarwal
Joseph Glanville
Hoseung Lee
Agustin Gonzalez
Yuanli Han
Himanshu
Parag Jain
Charles Smith