The current load process is:
Tablet Sink -> Tablet Channel Mgr -> Tablets Channel -> Delta Writer -> MemTable -> Flush to disk
In the path of Tablets Channel -> DeltaWriter -> MemTable -> Flush to disk, the following operations are performed:
Insert tuple into different memtables according to tablet ID
When the memtable size reaches the threshold, it is written to disk.
The above operations are equivalent to single thread execution for a single load task.
In fact, the insertion of memtable and the flush of memtable can be executed synchronously.
Perform these operation in single thread prevents the insertion of memtable from being delayed due to slow disk writing.
In the new implementation, I added a MemTableFlushExecutor class with a set of flush queues and corresponding worker threads.
By default, each data directory uses two worker threads for flush, which can be modified by the parameter flush_thread_num_per_store of BE.
DeltaWriter will push the full memtable to MemTableFlushExecutor for flush operation and generate a new memtable for receiving new data.
This design can improve the performance of load large files.
In single host testing, the time to load a 1GB text file is reduced from 48 seconds to 29 seconds.
1. Calculate cumulative point when loading tablet first time.
2. Simplify pick rowsets logic upon delete predicate.
3. Saving meta and modify rowsets only once after cumulative compaction.
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.
