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Another implementation of Apache Uniffle shuffle server (Single binary, no extra dependencies and quick)

Production ready, this project has been used for online shuffle storage service.

Benchmark report

Environment

type description
Software Uniffle 0.8.0 / Hadoop 3.2.2 / Spark 3.1.2
Hardware Machine 96 cores, 512G memory, 1T * 4 SATA SSD, network bandwidth 8GB/s
Hadoop Yarn Cluster 1 * ResourceManager + 40 * NodeManager, every machine 1T * 4 SATA SSD
Uniffle Cluster 1 * Coordinator + 1 * Shuffle Server, every machine 1T * 4 SATA SSD

Configuration

spark's conf

spark.executor.instances 400
spark.executor.cores 1
spark.executor.memory 2g
spark.shuffle.manager org.apache.spark.shuffle.RssShuffleManager
spark.rss.storage.type MEMORY_LOCALFILE

Rust-based shuffle-server conf

store_type = "MEMORY_LOCALFILE"
grpc_port = 21100
coordinator_quorum = ["xxxxx:21000"]
tags = ["riffle2", "datanode", "GRPC", "ss_v5"]

[memory_store]
capacity = "10G"
dashmap_shard_amount = 128

[localfile_store]
data_paths = ["/data1/uniffle/t1", "/data2/uniffle/t1", "/data3/uniffle/t1", "/data4/uniffle/t1"]
healthy_check_min_disks = 0
disk_max_concurrency = 2000

[hybrid_store]
memory_spill_high_watermark = 0.5
memory_spill_low_watermark = 0.2
memory_spill_max_concurrency = 1000

[metrics]
http_port = 19998
push_gateway_endpoint = "http://xxxxx/prometheus/pushgateway"

[runtime_config]
read_thread_num = 40
write_thread_num = 200
grpc_thread_num = 100
http_thread_num = 10
default_thread_num = 20
dispatch_thread_num = 10

GRPC_PARALLELISM=100 WORKER_IP=10.0.0.1 RUST_LOG=info ./uniffle-worker

TeraSort Result

type/buffer capacity 273G (compressed)
vanilla spark ESS 4.2min (1.3m/2.9m)
riffle(grpc) / 10g 4.0min (1.9m/2.1m)
riffle(grpc) / 300g 3.5min (1.4m/2.1m)
riffle(urpc) / 10g 3.8min (1.6m/2.2m)
riffle(urpc) / 300g 3.2min (1.2m/2.0m)
uniffle(grpc)/ 10g 4.0min (1.8m/2.2m)
uniffle(grpc)/ 300g 8.6min (2.7m/5.9m)
uniffle(netty)(default malloc) 10g 5.1min (2.7m/2.4m)
uniffle(netty)(jemalloc) 10g 4.5min (2.0m/2.5m)
uniffle(netty)(default malloc)/ 300g 4.0min (1.5m/2.5m)
uniffle(netty)(jemalloc)/ 300g 6.6min (1.9m/4.7m)

tips: the riffle's urpc implements the customized tcp stream's proto, that is named with the NETTY rpc type in java side.

Build

cargo build --release --features hdfs,jemalloc

Uniffle-x currently treats all compiler warnings as error, with some dead-code warning excluded. When you are developing and really want to ignore the warnings for now, you can use cargo --config 'build.rustflags=["-W", "warnings"]' build to restore the default behavior. However, before submit your pr, you should fix all the warnings.

Run

WORKER_IP={ip} RUST_LOG=info WORKER_CONFIG_PATH=./config.toml ./uniffle-worker

HDFS Setup

Benefit from the hdfs-native crate, there is no need to setup the JAVA_HOME and relative dependencies. If HDFS store is valid, the spark client must specify the conf of spark.rss.client.remote.storage.useLocalConfAsDefault=true

cargo build --features hdfs --release
# configure the kerberos
KRB5_CONFIG=/etc/krb5.conf KRB5CCNAME=/tmp/krb5cc_2002 LOG=info ./uniffle-worker

All config options

store_type = "MEMORY_LOCALFILE_HDFS"
grpc_port = 19999
coordinator_quorum = ["host1:port", "host2:port"]
urpc_port = 20000
http_monitor_service_port = 20010
heartbeat_interval_seconds = 2
tags = ["GRPC", "ss_v5", "GRPC_NETTY"]

[memory_store]
capacity = "1G"
buffer_ticket_timeout_sec = 300
buffer_ticket_check_interval_sec = 10
dashmap_shard_amount = 128

[localfile_store]
data_paths = ["/var/data/path1", "/var/data/path2"]
min_number_of_available_disks = 1
disk_high_watermark = 0.8
disk_low_watermark = 0.7
disk_max_concurrency = 2000
disk_write_buf_capacity = "1M"
disk_read_buf_capacity = "1M"
disk_healthy_check_interval_sec = 60

[hdfs_store]
max_concurrency = 50
partition_write_max_concurrency = 20

[hdfs_store.kerberos_security_config]
keytab_path = "/path/to/keytab"
principal = "principal@REALM"

[hybrid_store]
memory_spill_high_watermark = 0.8
memory_spill_low_watermark = 0.2
memory_single_buffer_max_spill_size = "1G"
memory_spill_to_cold_threshold_size = "128M"
memory_spill_to_localfile_concurrency = 4000
memory_spill_to_hdfs_concurrency = 500
huge_partition_memory_spill_to_hdfs_threshold_size = "64M"

[runtime_config]
read_thread_num = 100
localfile_write_thread_num = 100
hdfs_write_thread_num = 20
http_thread_num = 2
default_thread_num = 10
dispatch_thread_num = 100

[metrics]
push_gateway_endpoint = "http://example.com/metrics"
push_interval_sec = 10
labels = { env = "production", service = "my_service" }

[log]
path = "/var/log/my_service.log"
rotation = "Daily"

[app_config]
app_heartbeat_timeout_min = 5
huge_partition_marked_threshold = "1G"
huge_partition_memory_limit_percent = 0.75

[tracing]
jaeger_reporter_endpoint = "http://jaeger:14268"
jaeger_service_name = "my_service"

[health_service_config]
alive_app_number_max_limit = 100

Profiling

Heap profiling

  1. build with profile support 
    cargo build --release --features memory-prof
  2. Start with profile 
    curl localhost:20010/debug/heap/profile > heap.pb.gz
go tool pprof -http="0.0.0.0:8081" heap.pb.gz

CPU Profiling

  1. build with jemalloc feature

    cargo build --release --features jemalloc

  2. Paste following command to get cpu profile flamegraph

    go tool pprof -http="0.0.0.0:8081" http://{remote_ip}:8080/debug/pprof/profile?seconds=30
    • localhost:8080: riffle server.
    • remote_ip: pprof server address.
    • seconds=30: Profiling lasts for 30 seconds.

    Then open the URL :8081/ui/flamegraph in your browser to view the flamegraph: