boost::stacktrace::stacktrace() has memory leak, so use glog internal func to print stacktrace.
The reason for the memory leak of boost::stacktrace is that a state is saved in the thread local of each thread but not actively released. The test found that each thread leaked about 100M after calling boost::stacktrace.
refer to:
boostorg/stacktrace#118boostorg/stacktrace#111
mem tracker can be logically divided into 4 layers: 1)process 2)type 3)query/load/compation task etc. 4)exec node etc.
type includes
enum Type {
GLOBAL = 0, // Life cycle is the same as the process, e.g. Cache and default Orphan
QUERY = 1, // Count the memory consumption of all Query tasks.
LOAD = 2, // Count the memory consumption of all Load tasks.
COMPACTION = 3, // Count the memory consumption of all Base and Cumulative tasks.
SCHEMA_CHANGE = 4, // Count the memory consumption of all SchemaChange tasks.
CLONE = 5, // Count the memory consumption of all EngineCloneTask. Note: Memory that does not contain make/release snapshots.
BATCHLOAD = 6, // Count the memory consumption of all EngineBatchLoadTask.
CONSISTENCY = 7 // Count the memory consumption of all EngineChecksumTask.
}
Object pointers are no longer saved between each layer, and the values of process and each type are periodically aggregated.
other fix:
In [fix](memtracker) Fix transmit_tracker null pointer because phamp is not thread safe #13528, I tried to separate the memory that was manually abandoned in the query from the orphan mem tracker. But in the actual test, the accuracy of this part of the memory cannot be guaranteed, so put it back to the orphan mem tracker again.
# Proposed changes
This PR fixed lots of issues when building from source on macOS with Apple M1 chip.
## ATTENTION
The job for supporting macOS with Apple M1 chip is too big and there are lots of unresolved issues during runtime:
1. Some errors with memory tracker occur when BE (RELEASE) starts.
2. Some UT cases fail.
...
Temporarily, the following changes are made on macOS to start BE successfully.
1. Disable memory tracker.
2. Use tcmalloc instead of jemalloc.
This PR kicks off the job. Guys who are interested in this job can continue to fix these runtime issues.
## Use case
```shell
./build.sh -j 8 --be --clean
cd output/be/bin
ulimit -n 60000
./start_be.sh --daemon
```
## Something else
It takes around _**10+**_ minutes to build BE (with prebuilt third-parties) on macOS with M1 chip. We will improve the development experience on macOS greatly when we finish the adaptation job.
disable page cache by default
disable chunk allocator by default
not use chunk allocator for vectorized allocator by default
add a new config memory_linear_growth_threshold = 128Mb, not allocate memory by RoundUpToPowerOf2 if the allocated size is larger than this threshold. This config is added to MemPool, ChunkAllocator, PodArray, Arena.
1. HttpServer threads allocate bytebuffer and put them into streamload pipe, but scanner thread release them with query tracker.
2. We can assume brpc allocate memory in doris thread.
Above problems leads to wrong result of memtracker.
The mem hook consumes the orphan tracker by default. If the thread does not attach other trackers, by default all consumption will be passed to the process tracker through the orphan tracker.
In real time, consumption of all other trackers + orphan tracker consumption = process tracker consumption.
Ideally, all threads are expected to attach to the specified tracker, so that "all memory has its own ownership", and the consumption of the orphan mem tracker is close to 0, but greater than 0.
Following the iteration order of the hash table will result in out-of-order access to aggregate states, which is very inefficient.
Traversing aggregate states in memory write order can significantly improve memory read efficiency.
Test
hash table items count: 3.35M
Before this optimization: insert keys into column takes 500ms
With this optimization only takes 80ms
Disable Chunk Allocator in Vectorized Allocator, this will reduce memory cache.
For high concurrent queries, using Chunk Allocator with vectorized Allocator can reduce the impact of gperftools tcmalloc central lock.
Jemalloc or google tcmalloc have core cache, Chunk Allocator may no longer be needed after replacing gperftools tcmalloc.
Currently, only the virtual memory used by the query can be tracked through the tcmalloc hook. When the memory is not fully used after the application, the recorded virtual memory will be larger than the physical memory.
At present, it is mainly because PODArray does not memset 0 when applying for memory, and blocks applied for through PODArray in places such as VOlapScanNode::_free_blocks are usually used for memory reuse and cannot be fully used.
In the strict memory usage mode of STRICT_MEMORY_USE=ON, when the capacity of the vectorized Hash Table is greater than 2G, it starts to grow when 75% of the capacity is satisfied, the memory usage of the vectorized Join becomes 50% of the previous value.
STRICT_MEMORY_USE=ON` expects BE to use less memory, and gives priority to ensuring stability when the cluster memory is limited.
* add volnitsky substr algorithm
* replace std::search with volnitsky search algorithm in StringSearch
* optimize substring for constant_substring_fn case
use long run length search for performance
