1. use rlock in most logic instead of wrlock
2. filter stale rowset's delete bitmap in save meta
3. add a delete_bitmap lock to handle compaction and publish_txn confict
Co-authored-by: yixiutt <yixiu@selectdb.com>
There are currently many types of ScanNodes in Doris. And most of the logic of these ScanNodes is the same, including:
Runtime filter
Predicate pushdown
Scanner generation and scheduling
So I intend to unify the common logic of all ScanNodes.
Different data sources only need to implement different Scanners for data access.
So that the future optimization for scan can be applied to the scan of all data sources,
while also reducing the code duplication.
This PR mainly adds 4 new class:
VScanner
All Scanners' parent class. The subclasses can inherit this class to implement specific data access methods.
VScanNode
The unified ScanNode, and is responsible for common logic including RuntimeFilter, predicate pushdown, Scanner generation and scheduling.
ScannerContext
ScannerContext is responsible for recording the execution status
of a group of Scanners corresponding to a ScanNode.
Including how many scanners are being scheduled, and maintaining
a producer-consumer blocks queue between scanners and scan nodes.
ScannerContext is also the scheduling unit of ScannerScheduler.
ScannerScheduler schedules a ScannerContext at a time,
and submits the Scanners to the scanner thread pool for data scanning.
ScannerScheduler
Unified responsible for all Scanner scheduling tasks
Test:
This work is still in progress and default is disabled.
I tested it with jmeter with 50 concurrency, but currently the scanner is just return without data.
The QPS can reach about 9000.
I can't compare it to origin implement because no data is read for now. I will test it when new olap scanner is ready.
Co-authored-by: morningman <morningman@apache.org>
- add an interface ExpectsInputTypes to Expression
- add an interface ImplicitCastInputTypes to Expression
- add a Expression rewrite rule for type coercion
- add a Check Analysis Rule to check whether Plan is Semantically correct
if Expression implements ImplicitCastInputTypes, type coercion rule will automatic rewrite its children that casting it to the most suitable type.
If Expression implements ExpectsInputTypes, Check Analysis will check its children's type whether accepted by expects input types.
1. Spark can set the timestamp precision by the following configuration:
spark.sql.parquet.outputTimestampType = INT96(NANOS), TIMESTAMP_MICROS, TIMESTAMP_MILLIS
DATETIME V1 only keeps the second precision, DATETIME V2 keeps the microsecond precision.
2. If using DECIMAL V2, the BE saves the value as decimal128, and keeps the precision of decimal as (precision=27, scale=9). DECIMAL V3 can maintain the right precision of decimal
Stream load will ignore invisible columns if no http header columns
specified, but in some case user cannot get all columns if columns
changed frequently。
Add a hidden_columns header to support hidden columns import。User can
set hidden_columns such as __DORIS_DELETE_SIGN__ and add this column
in stream load data so we can delete this line.
For example:
curl -u root -v --location-trusted -H "hidden_columns: __DORIS_DELETE_SIGN__" -H
"format: json" -H "strip_outer_array: true" -H "jsonpaths: [\"$.id\",
\"$.name\",\"$.__DORIS_DELETE_SIGN__\"]" -T 1.json
http://{beip}:{be_port}/api/test/test1/_stream_load
Co-authored-by: yixiutt <yixiu@selectdb.com>
