Early Design Documentation: https://shimo.im/docs/DT6JXDRkdTvdyV3G
Implement a new way of memory statistics based on TCMalloc New/Delete Hook,
MemTracker and TLS, and it is expected that all memory new/delete/malloc/free
of the BE process can be counted.
Modify the implementation of MemTracker:
1. Simplify a lot of useless logic;
2. Added MemTrackerTaskPool, as the ancestor of all query and import trackers, This is used to track the local memory usage of all tasks executing;
3. Add cosume/release cache, trigger a cosume/release when the memory accumulation exceeds the parameter mem_tracker_consume_min_size_bytes;
4. Add a new memory leak detection mode (Experimental feature), throw an exception when the remaining statistical value is greater than the specified range when the MemTracker is destructed, and print the accurate statistical value in HTTP, the parameter memory_leak_detection
5. Added Virtual MemTracker, cosume/release will not sync to parent. It will be used when introducing TCMalloc Hook to record memory later, to record the specified memory independently;
6. Modify the GC logic, register the buffer cached in DiskIoMgr as a GC function, and add other GC functions later;
7. Change the global root node from Root MemTracker to Process MemTracker, and remove Process MemTracker in exec_env;
8. Modify the macro that detects whether the memory has reached the upper limit, modify the parameters and default behavior of creating MemTracker, modify the error message format in mem_limit_exceeded, extend and apply transfer_to, remove Metric in MemTracker, etc.;
Modify where MemTracker is used:
1. MemPool adds a constructor to create a temporary tracker to avoid a lot of redundant code;
2. Added trackers for global objects such as ChunkAllocator and StorageEngine;
3. Added more fine-grained trackers such as ExprContext;
4. RuntimeState removes FragmentMemTracker, that is, PlanFragmentExecutor mem_tracker, which was previously used for independent statistical scan process memory, and replaces it with _scanner_mem_tracker in OlapScanNode;
5. MemTracker is no longer recorded in ReservationTracker, and ReservationTracker will be removed later;
We make all MemTrackers shared, in order to show MemTracker real-time consumptions on the web.
As follows:
1. nearly all MemTracker raw ptr -> shared_ptr
2. Use CreateTracker() to create new MemTracker(in order to add itself to its parent)
3. RowBatch & MemPool still use raw ptrs of MemTracker, it's easy to ensure RowBatch & MemPool destructor exec
before MemTracker's destructor. So we don't change these code.
4. MemTracker can use RuntimeProfile's counter to calc consumption. So RuntimeProfile's counter need to be shared
too. We add a shared counter pool to store the shared counter, don't change other counters of RuntimeProfile.
Note that, this PR doesn't change the MemTracker tree structure. So there still have some orphan trackers, e.g. RowBlockV2's MemTracker. If you find some shared MemTrackers are little memory consumption & too time-consuming, you could make them be the orphan, then it's fine to use the raw ptr.
#3479
Here I try to explain the cause of the problem and how to fix it.
**The Cause of The problem**
Take the case in issue(#3479 ) as an example:
The general results are as follows:
```
GET table/_doc/_search
{"query":{"match_all":{}},"stored_fields":"_none_","docvalue_fields":["k1"],"sort":["_doc"],"size":100}
{
"took": 6,
"timed_out": false,
"_shards": {
……
},
"hits": {
"total": 3,
"max_score": null,
"hits": [
{
"_index": "table",
"_score": null,
"sort": [
0
]
},
{
"_index": "table",
"_score": null,
"fields": {
"k1": [
"kkk1"
]
},
"sort": [
0
]
},
{
"_index": "table",
"_score": null,
"sort": [
0
]
}
]
}
}
```
But in Doris on ES，Be fetched data parallelly on all shards, and use `filter_path` to reduce the network cost. The process will be as follows:
```
GET table/_doc/_search?preference=_shards:1&filter_path=_scroll_id,hits.hits._source,hits.total,_id,hits.hits._source.fields,hits.hits.fields
{"query":{"match_all":{}},"stored_fields":"_none_","docvalue_fields":["k1"],"sort":["_doc"],"size":100}
{
"hits": {
"total": 0
}
}
GET table/_doc/_search?preference=_shards:2&filter_path=_scroll_id,hits.hits._source,hits.total,_id,hits.hits._source.fields,hits.hits.fields
{"query":{"match_all":{}},"stored_fields":"_none_","docvalue_fields":["k1"],"sort":["_doc"],"size":100}
{
"hits": {
"total": 1
}
}
GET table/_doc/_search?preference=_shards:3&filter_path=_scroll_id,hits.hits._source,hits.total,_id,hits.hits._source.fields,hits.hits.fields
{"query":{"match_all":{}},"stored_fields":"_none_","docvalue_fields":["k1"],"sort":["_doc"],"size":100}
{
"hits": {
"total": 1,
"hits": [
{
"fields": {
"k1": [
"kkk1"
]
}
}
]
}
}
```
*Scan-Worker On BE which processed result of shard2 will failed.*
**The reasons are as follows:**
1. "filter_path" causes the hits.hits object not exist.
2. In the current implementation, if there are some data rows（total > 0）, the hits.hits. object must be an array
**How To Fix it**
Two Method:
1. modify "filter_path" to contain the hits.
Pros: Fixed Code is very simple
Cons: More network cost
2. Deal with the case where fields are missing in a batch.
Pros: No loss of performance
Cons: Code is more complex
Performance first, I use Method2.
**Design**
1. Add a variable "_doc_value_mode" into Class "EsScrollParser" to =indicate whether the data processed by this parser is doc_value_mode or not.
2. "_doc_value_mode" is passed from ESScollReader <- ESScanner <- ScrollQueryBuilder::build() that determines whether DSL is enable doc_value_mode
3. When hits.hits of response from ES is empty and total > 0. We know there are data lines, but the corresponding fields do not exist. EsScrollParser will use "_doc_value_mode" and _total to construct _total lines which fields are assigned with 'NULL'
Pure DocValue optimization for doris-on-es
Future todo:
Today, for every tuple scan we check if pure_docvalue is enabled, this is not reasonable, should check pure_docvalue enabled for one whole scan outside, I will add this todo in future
