Raw_batch_get 99% gRPC duration > 100ms

This topic has been translated from a Chinese forum by GPT and might contain errors.

Original topic: raw_batch_get 99% gRPC duration > 100ms+

| username: ycong

【TiDB Usage Environment】Production Environment
【TiDB Version】v7.5.0
【Reproduction Path】
【Encountered Problem: Problem Phenomenon and Impact】
raw_batch_get 10 keys, average within 2ms, 99% greater than 100ms. No CPU or IO bottleneck, found that snapshot takes a lot of time. Suspect that LSM scanning SST files causes long-tail effect, can parameters be adjusted for optimization?
【Resource Configuration】

Disk is using nvme ssd, ali PL3 performance is not an issue
【Attachments: Screenshots/Logs/Monitoring】

| username: WalterWj | Original post link

It looks like the higher the pressure, the higher the CPU usage, and the longer the response time, which seems expected. When the 99th percentile duration reaches 100ms, the traffic also reaches 30MB, and the CPU usage is around 15vc. This seems relatively expected.

| username: WalterWj | Original post link

You can check this part: TiKV 线程池性能调优 | PingCAP 文档中心. See if the shared thread pool is enabled and how much CPU is allocated. Check if the grpc CPU has become a bottleneck. You can try increasing some of the default CPU configurations to see the effect.

| username: ycong | Original post link

We are using the Java client to directly interact with TiKV, utilizing raw_batch_put and raw_batch_get, without involving the TiDB role.

The thread pool settings are quite large, and the CPU usage is only around 40%, not yet reaching 80%. However, the 99% duration has increased to over 100ms.

raftstore.apply-pool-size: 4
raftstore.raft-base-tick-interval: 2s
raftstore.store-pool-size: 8
resolved-ts.advance-ts-interval: 20s
rocksdb.defaultcf.soft-pending-compaction-bytes-limit: 256G
rocksdb.max-background-jobs: 16
rocksdb.max-sub-compactions: 8
rocksdb.wal-recovery-mode: 0
server.grpc-concurrency: 8
storage.api-version: 2
storage.enable-ttl: true
storage.scheduler-pending-write-threshold: 200MB
storage.scheduler-worker-pool-size: 4

| username: WalterWj | Original post link

The higher the pressure, the higher the duration, which is expected. It’s not that the CPU has no bottleneck and the duration remains unchanged under pressure.

You see, when the pressure is high, the raft store CPU seems to exceed 200%. Check if this helps:

| username: ycong | Original post link

store-io-pool-size=2, after reloading, P99 did not show significant improvement.

| username: WalterWj | Original post link

You can adjust the topology of TiKV, enable NUMA on the server, and bind cores according to the CPU node. For example, if there are two nodes, you can deploy two instances of TiKV, each bound to one node. This should optimize the duration to some extent.

| username: ycong | Original post link

There are many regions, try increasing the region size to reduce Raft overhead.

set config tikv coprocessor.region-max-size=‘384MB’;
set config tikv coprocessor.region-split-size=‘256MB’;

| username: 有猫万事足 | Original post link

I feel that your configuration is too small.

storage.scheduler-worker-pool-size: 4

As you mentioned, the snapshot is taking a long time.

You can see on this page, as shown in the figure below:

The red circle is the snapshot, which belongs to scheduler_command.



  • The number of threads in the Scheduler thread pool. Scheduler threads are mainly responsible for transaction consistency checks before writing. If the number of CPU cores is greater than or equal to 16, the default is 8; otherwise, the default is 4. When adjusting the size of the scheduler thread pool, please refer to TiKV Thread Pool Tuning.
  • Default value: 4
  • Adjustable range: [1, MAX(4, CPU)]. Where MAX(4, CPU) means: if the number of CPU cores is less than 4, take 4; if the number of CPU cores is greater than 4, take the number of CPU cores.

Although the recommended configuration is 4, I see that you have set several thread pools quite boldly. Maybe you have more CPU resources. Can you try setting it larger than 4 and see how it works?

| username: ycong | Original post link

Previously, the storage.scheduler-worker-pool-size configuration was set very high. Later, it was found that this thread was idle, so to avoid excessive context switching, it was changed to 4.

| username: ycong | Original post link

Thank you for the attention, Cat. As shown in my screenshot, the slowdown occurs at the snapshot (Replicate Raft Log + Propose Wait + xxx). Both raw_get and raw_batch_get in the source code will get a snapshot. Can this snapshot retrieval be sped up?

let snapshot =
    Self::with_tls_engine(|engine| Self::snapshot(engine, snap_ctx)).await?;
let buckets = snapshot.ext().get_buckets();
let store = RawStore::new(snapshot, api_version);

| username: 有猫万事足 | Original post link

tikv_storage_engine_async_request_duration_seconds{type=“snapshot”} =
tikv_coprocessor_request_wait_seconds{type=“snapshot”} =
tikv_raftstore_request_wait_time_duration_secs +
tikv_raftstore_commit_log_duration_seconds +
get snapshot from rocksdb duration

Now it is
get snapshot from rocksdb duration

Although the conclusion in the documentation is

Getting a snapshot from RocksDB is usually a quick operation, so the time taken for get snapshot from rocksdb duration can be ignored.

But according to this monitoring graph, we seem to be able to draw only one conclusion. That is, get snapshot from rocksdb duration takes about 50ms.

Let’s see if any experts can think of a solution. I don’t have a good idea right now.

| username: pingyu | Original post link

Based on the currently provided information, the long-tail latency of raw_batch_get is mainly caused by the increase in CPU usage due to raw_batch_put. Specifically:

  1. According to experience, to maintain stable long-tail latency, CPU usage should be controlled below 40%. Additionally, vCores cannot actually achieve twice the performance of physical cores, usually only about 1.5 times. Since vCores on the same physical core share registers, cache, and other hardware, increasing throughput will also increase long-tail latency (refer to https://www.sciencedirect.com/science/article/pii/S0167739X22000334?via%3Dihub). Therefore, for a 24 (48 vCore) model, it is recommended to control CPU utilization below 1200% if possible, or even disable hyper-threading.

  2. In heterogeneous mixed deployments, long-tail latency may be concentrated on the 16 (32 vCore) models, with monitoring showing that one of the CPUs has exceeded 1300% usage. It is recommended to check if the long-tail latency is concentrated on a few machines.

  3. The snapshot process observed in monitoring involves read index (see TiKV 源码解析系列文章(十九)read index 和 local read 情景分析 | PingCAP), mainly handled in Raftstore, which is sensitive to CPU usage and network latency.

  4. Increasing region size may negatively impact long-tail latency because Raft within the same region can only process serially, and increasing data volume may increase long-tail latency.


  1. Peak shaving for raw_batch_put, controlling CPU usage (calculated by physical cores) below 50%. Additionally, if possible, scatter raw_batch_put writes to avoid concentrating on a few regions at the same time.

  2. Deploy TiKV independently and use the same model.

  3. Adjust the model configuration, as the current model’s CPU is relatively small compared to memory. TiKV generally recommends a CPU to Memory ratio of 1:4. When using NVMe, the effect of a large block cache is not very significant. Adjusting to 32C/128GB with similar costs is believed to improve long-tail latency.