Use same UUID as query ID and load ID of a load execution plan.
Each load execution plan has a load ID, and as a plan, there is also a query ID.
We can use same UUID as query ID and load ID, for tracing the load process more easily.
Change the load ID when retrying a load execution plan.
When a load execution plan retry, the load ID should be changed, otherwise BE can not
distinguish the old and new load requests.
Cancel the running loading task when cancelling the broker load.
When user cancel a broker load, the running loading task should also be cancelled, or
it may occupies the worker thread for a long time.
Remove the unnecessary query report when doing load execution plan.
Only the last query report is needed.
Add a new BE config tablet_writer_rpc_timeout_sec.
It is used for RPC of tablet sink. The default is 600 seconds. which is long enough for flushing
about 6GB data. The long timeout config will reduce the possibility of encountering fail to send batch error when loading.
Use streaming_load_max_mb instead of mini_load_max_mb in BE config.
Add more logs for tracing a broker load process easily.
The Operator wants to known when the job being scheduled as PENDING
and LOADING. And how long it takes to finish these sub states.
Also add 2 metrics on BE to monitor the memtable's flush time.
`memtable_flush_total` and `memtable_flush_duration_us`
When query result reach limit, the Coordinator in FE will send a cancel
request to BE to cancel the query. And when being cancelled, BE will report
query status to FE for debug purpose. But actually it is not necessary
and will generate too many logs.
So I add a CancelReason to distinguish the difference between 'normally'
cancellation and 'internal error' cancellation. if 'normally' cancelled,
no status will be reported from BE.
When query reach limit, or user cancel it actively, it is being cancelled 'normally'.
Otherwise, the query is cancelled due to internal error, which will need
a report from BE.
Mini load is now using stream load framework. But we should keep the
mini load return behavior and result json format be same as old.
So PUBLISH_TIMEOUT error should be treated as OK in mini load.
Also add 2 counters for OlapTableSink profile:
SerializeBatchTime: time of serializing all row batch.
WaitInFlightPacketTime: time of waiting last send packet
Mini load timeout needs to be added in plan options.
The timeout property has been added in request of process put.
Otherwise, the timeout of mini load is useless.
Add log of label, txn and query id in mini load
NOTE: This patch would modify all Backend's data.
And this will cause a very long time to restart be.
So if you want to interferer your product environment,
you should upgrade backend one by one.
1. Refactoring be is to clarify the structure the codes.
2. Use unique id to indicate a rowset.
Nameing rowset with tablet_id and version will lead to
many conflicts among compaction, clone, restore.
3. Extract an rowset interface to encapsulate rowsets
with different format.
Currently, the load error log on BE will be cleaned along with the
intermediate data of load, configured by 'load_data_reserve_hours'.
Sometimes user want to reserve the error log for longer time.
* This commit has brought contribution to streaming mini load
The operation of streaming mini load is sames as previous. Also, user can check the load by frontend.
The difference is that streaming mini load finish the task before reply of REST API while the non-streaming only register a load.
* When updating doris
Updating fe or be firstly are also supported. After fe and be are updated, the streaming mini load will take effect.
* For multi mini load
The non-streaming mini load still has been used by multi mini load. The behavior of multi mini load has not been changed.
* Add a interface named isSupportedFunction
This function is used to protect the correctness of new feature which consists of be and fe during updaing.
Before RowBlock will reserve memory for all columns in schema, even if
it is not queried. Which will cause bad performance when quering wide
table.
In this patch, RowBlock will reserve memory for needed columns. In a
case, this reduce ConvertBatchTime from 10s to 60ms when quering a wide
table who has 178 columns.
#1236
1. get_json_xxx() now support using quoto to escape dot
2. Implement json_path_prepare() function to preprocess json_path
Performance of get_json_string() on 1000000 rows reduces from 2.27s to 0.27s
* Change label of broker load txn
1. put broker load label into txn label
2. fix the bug of `label is already used`
3. fix partition error of new broker load
* Fix count error in mini load and broker load
There are three params (num_rows_load_total, num_rows_load_filtered, num_rows_load_unselected) which are used to count dpp.norm.ALL and dpp.abnorm.ALL.
num_rows_load_total is the number rows of source file.
num_rows_load_unselected is the not satisfied (where conjuncts) rows of num_rows_load_total
num_rows_load_filtered is the rows (quality not good enough) of (num_rows_load_total-num_rows_load_unselected)
* Enhence the usabilities
1. Add metrics to monitor transactions and steaming load process in BE.
2. Modify BE config 'result_buffer_cancelled_interval_time' to 300s.
3. Modify FE config 'enable_metric_calculator' to true.
4. Add more log for tracing broker load process.
5. Modify the query report process, to cancel query immediately if some instance failed.
* Fix bugs
1. Avoid NullPointer when enabling colocation join with broker load
2. Return immediately when pull load task coordinator execution failed
1. Use a data consumer group to share a single stream load pipe with multi data consumers. This will increase the consuming speed of Kafka messages, as well as reducing the task number of routine
load job.
Test results：
* 1 consumer, 1 partitions:
consume time: 4.469s, rows: 990140, bytes: 128737139. 221557 rows/s, 28M/s
* 1 consumer, 3 partitions:
consume time: 12.765s, rows: 2000143, bytes: 258631271. 156689 rows/s, 20M/s
blocking get time(us): 12268241, blocking put time(us): 1886431
* 3 consumers, 3 partitions:
consume time(all 3): 6.095s, rows: 2000503, bytes: 258631576. 328220 rows/s, 42M/s
blocking get time(us): 1041639, blocking put time(us): 10356581
The next 2 cases show that we can achieve higher speed by adding more consumers. But the bottle neck transfers from Kafka consumer to Doris ingestion, so 3 consumers in a group is enough.
I also add a Backend config `max_consumer_num_per_group` to change the number of consumers in a data consumer group, and default value is 3.
In my test(1 Backend, 2 tablets, 1 replicas), 1 routine load task can achieve 10M/s, which is same as raw stream load.
2. Add OFFSET_BEGINNING and OFFSET_END support for Kafka routine load
1. stream load executor will abort txn when no correct data in task
2. change txn label to DebugUtil.print(UUID) which is same as task id printed by be
3. change print uuid to hi-lo
1. init cmt offset in stream load context
2. init default max error num = 5000 rows / per 10000 rows
3. add log builder for routine load job and task
4. clone plan fragment param for every task
5. be does not throw too many filter rows while the init max error ratio is 1
