* Various documentation updates.
1) Split out "data management" from "ingestion". Break it into thematic pages.
2) Move "SQL-based ingestion" into the Ingestion category. Adjust content so
all conceptual content is in concepts.md and all syntax content is in reference.md.
Shorten the known issues page to the most interesting ones.
3) Add SQL-based ingestion to the ingestion method comparison page. Remove the
index task, since index_parallel is just as good when maxNumConcurrentSubTasks: 1.
4) Rename various mentions of "Druid console" to "web console".
5) Add additional information to ingestion/partitioning.md.
6) Remove a mention of Tranquility.
7) Remove a note about upgrading to Druid 0.10.1.
8) Remove no-longer-relevant task types from ingestion/tasks.md.
9) Move ingestion/native-batch-firehose.md to the hidden section. It was previously deprecated.
10) Move ingestion/native-batch-simple-task.md to the hidden section. It is still linked in some
places, but it isn't very useful compared to index_parallel, so it shouldn't take up space
in the sidebar.
11) Make all br tags self-closing.
12) Certain other cosmetic changes.
13) Update to node-sass 7.
* make travis use node12 for docs
Co-authored-by: Vadim Ogievetsky <vadim@ogievetsky.com>
* remove things that do not apply
* fix more things
* pin node to a working version
* fix
* fixes
* known issues tidy up
* revert auto formatting changes
* remove management-uis page which is 100% lies
* don't mention the Coordinator console (that no longer exits)
* goodies
* fix typo
Co-authored-by: Clint Wylie <cjwylie@gmail.com>
Co-authored-by: Victoria Lim <vtlim@users.noreply.github.com>
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
Co-authored-by: brian.le <brian.le@imply.io>
Two improvements:
- Use a realistic targetRowsPerSegment, so if people copy and paste
the example from the docs, it will generate reasonable segments.
- Spell "countryName" correctly.
* Add clarification for combining input source
* Update inputFormat note
* Update maxNumConcurrentSubTasks note
* Fix broken link
* Update docs/ingestion/native-batch-input-source.md
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
In a heterogeneous environment, sometimes you don't have control over the input folder. Upstream can put any folder they want. In this situation the S3InputSource.java is unusable.
Most people like me solved it by using Airflow to fetch the full list of parquet files and pass it over to Druid. But doing this explodes the JSON spec. We had a situation where 1 of the JSON spec is 16MB and that's simply too much for Overlord.
This patch allows users to pass {"filter": "*.parquet"} and let Druid performs the filtering of the input files.
I am using the glob notation to be consistent with the LocalFirehose syntax.
* Improved docs for range partitioning.
1) Clarify the benefits of range partitioning.
2) Clarify which filters support pruning.
3) Include the fact that multi-value dimensions cannot be used for partitioning.
* Additional clarification.
* Update other section.
* Another adjustment.
* Updates from review.
* Update ingestion-spec.md
Added indexSpecForIntermediatePersists as a common configuration property.
* Update ingestion-spec.md
Amended to remove "below" and add link to the table.
* Update ingestion-spec.md
Removed passive.
* 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>
* Update math-expr.md
Link back to transformSpec
* Update ingestion-spec.md
Moved info about using the timestamp inside transforms into the actual timestamp section.
* Update ingestion-spec.md
Active language.
* Update ingestion-spec.md
Added best practice point to dimensions description.
* Update docs/ingestion/ingestion-spec.md
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* Tombstone support for replace functionality
* A used segment interval is the interval of a current used segment that overlaps any of the input intervals for the spec
* Update compaction test to match replace behavior
* Adapt ITAutoCompactionTest to work with tombstones rather than dropping segments. Add support for tombstones in the broker.
* Style plus simple queriableindex test
* Add segment cache loader tombstone test
* Add more tests
* Add a method to the LogicalSegment to test whether it has any data
* Test filter with some empty logical segments
* Refactor more compaction/dropexisting tests
* Code coverage
* Support for all empty segments
* Skip tombstones when looking-up broker's timeline. Discard changes made to tool chest to avoid empty segments since they will no longer have empty segments after lookup because we are skipping over them.
* Fix null ptr when segment does not have a queriable index
* Add support for empty replace interval (all input data has been filtered out)
* Fixed coverage & style
* Find tombstone versions from lock versions
* Test failures & style
* Interner was making this fail since the two segments were consider equal due to their id's being equal
* Cleanup tombstone version code
* Force timeChunkLock whenever replace (i.e. dropExisting=true) is being used
* Reject replace spec when input intervals are empty
* Documentation
* Style and unit test
* Restore test code deleted by mistake
* Allocate forces TIME_CHUNK locking and uses lock versions. TombstoneShardSpec added.
* Unused imports. Dead code. Test coverage.
* Coverage.
* Prevent killer from throwing an exception for tombstones. This is the killer used in the peon for killing segments.
* Fix OmniKiller + more test coverage.
* Tombstones are now marked using a shard spec
* Drop a segment factory.json in the segment cache for tombstones
* Style
* Style + coverage
* style
* Add TombstoneLoadSpec.class to mapper in test
* Update core/src/main/java/org/apache/druid/segment/loading/TombstoneLoadSpec.java
Typo
Co-authored-by: Jonathan Wei <jon-wei@users.noreply.github.com>
* Update docs/configuration/index.md
Missing
Co-authored-by: Jonathan Wei <jon-wei@users.noreply.github.com>
* Typo
* Integrated replace with an existing test since the replace part was redundant and more importantly, the test file was very close or exceeding the 10 min default "no output" CI Travis threshold.
* Range does not work with multi-dim
Co-authored-by: Jonathan Wei <jon-wei@users.noreply.github.com>
* 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
* 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
* Update rollup.md
Added SE tip around roll-up.
* Update docs/ingestion/rollup.md
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
Co-authored-by: Charles Smith <techdocsmith@gmail.com>
* add impl
* fix checkstyle
* add test
* add test
* add unit tests
* fix unit tests
* fix unit tests
* fix unit tests
* add IT
* add IT
* add comments
* fix spelling
* 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.
Vadim Ogievetsky
Gian Merlino
Jill Osborne
Victoria Lim
David Hergenroeder
Katya Macedo
Charles Smith
Atul Mohan
Didip Kerabat
Kashif Faraz
Peter Marshall
317brian
mark-imply
Agustin Gonzalez
Jihoon Son
Jonathan Wei
Frank Chen
shallada
Maytas Monsereenusorn
jacobtolar
lokesh-lingarajan