Xin Wang(Apache Storm Committer/PMC member)'s topic covered the relations between streaming and messaging platform, and the challenges and tips in Storm usage.

1. Realtime Statistics based on Apache Storm and RocketMQ
- Xin Wang
Dec.16, 2017, Shenzhen, Apache RocketMQ Meetup

2. Xin Wang
• Apache Storm Committer & PMC member
• Five years distributed system experience
• Love open source & community
• Focus on distributed technologies, especially stream processing
• https://github.com/vesense

3. Streaming and batch which come from different worlds,
use different ways to solve different problems.
- Xin

4. Index
Part-1: Streaming Ecosystem
Part-2: Stateful Statistics based on Storm & RocketMQ
Part-3: Best Practices

5. 01
Streaming Ecosystem

6. The Streaming Ecosystem
Collector
Messaging
SQL
Streaming-
Connector
Streaming-
Connector
Storage
APP
Streaming-
State
Schema-
Registry
CEP ML ...
Stream API
Runtime
Deploy: Local, Cluster, Cloud
Streaming-
Manager
Streaming
...
Messaging: apache/kafka,rocketmq,pulsar
Streaming: apache/storm,flink,spark-streaming,kafka-streams
Schema-Registry: hortonworks/registry, confluentinc/schema-registry
Streaming-Manager: hortonworks/streamline

7. Which one is better for me?
• Simple API
• Fault-tolerant/Stable
• Scalable
• Performance(high throughput & low latency)
• Guarantees: at-least-once/exactly-once
• Mature
• Ecosystem
• Operation and Maintenance
• Support
• Code

8. Storm 2.0
• Port Clojure to Java
• Unified Stream API
• Storm-SQL Improvements
• Metrics V2
• Threading model Redesign
• Lambda Expression Support - bolt: `tuple -> System.out.println(tuple)`
• Apache Beam Runner
• Worker Classloader Isolation
• Dynamic Topology Updates
• ......

9. 02
Stateful Statistics based on Storm & RocketMQ

10. Realtime Architecture
Apache HBase
MySQL
S
S
P R
Source
Topic
Retry
Topic
Sink Topic
Apache RocketMQ
Apache RocketMQ
Apache Storm

11. Stateful Statistics Challenges
1 2 3
1 2 3
3 2 1
time
1 2
3
~
loss
duplicating
out-of-order
mutex
Q:
complex, state machine + streaming
open source middleware?
A:
• loss -> compensating
• s1 -> s2 on condition when e3
• duplicating -> idempotent
• exists(key)
• out-of-order -> compensating+idempotent
• mutex -> +/-
• s1++ && s2--
1 2 3 expected

12. Stateful Event Counting: Alien
Alien? a stateful event counting middleware.
• Support event loss, duplicating, out-of-order, mutex
• Support time/event Window API
• Support integrating with streaming systems
• Support dimension changing
• Support sync/async snapshot storage
• Support user defined State, Snapshot Serializer
• Support state REST interfaces
• ...
Alien alien = Alien.createAlien()
.withState(new LocalMemoryState())
.withWindow(new TimeWindow(2){
@Override
public void accept(Map<String,View> views) {
View view = views.get(“report”);
List<Row> rows = view.getRows();
for (Row r : rows) {
println(r.getDimensions() + “->” + r.getMetrics());
}
}
});
alien.putEvent(new Event("name","key") );

13. 03
Best Practices

14. Best Practices
• Worker heavy GCs:
• worker restart, heartbeat timeout, bad performance -> take care of your heap memory usage. Do you use local
caches? Reasonable JVM options? e.g. -XX:CMSInitiatingOccupancyFraction
• Topology design vs performance
• bad performance -> put the lightweight logics into the same bolt/operator
• Too many executors/tasks
• high cluster CPU load, bad performance -> tuning the number of threads:
for CPU-intensive task: task parallelism <= vcore,
for IO-intensive task: vcore <= task parallelism <= N*vcore.
Warn: runnable sun.nio.ch.EPollArrayWrapper.epollWait
CPU: user cpu or sys cpu? Load: runnable task or io task?
Amdahl law: Non-Parallelizable + Parallelizable
• Data hot point / data skew
• some nodes have bad performance -> choose the right hash key, two-phase aggregation, or use micro-batch
• Big objects serialization:
• bad performance -> reduce the size of objects, and enable kryo registry(from 55ms to 11ms after kryo registry)
• Too many logs:
• bad performance -> never log the logs unnecessary

15. Data Hot Point / Data Skew
Q:
partition = hash(key) % N
A:
• Choose the right hash key
• mapreduce from history?
• key == null?
• Two-phase aggregation
• Use micro-batch / local-reduce
S
P
P
S
P
P
S
P
P
G
k1
k2
k1+salt1
k1+salt2
k1
k2micro-batch
num(k1)
num(P)
num(S)

16. RocketMQ-Streaming Integration
RocketMQ-Storm: https://github.com/apache/storm/tree/master/external/storm-rocketmq
• RocketMQSpout - Now only RocketMQ push mode supported, pull mode is in the plan. The default Deserializer is
StringScheme, you can override the value by setting `RocketMQConfig.SCHEME`.
• RocketMQBolt - Async sending by default, or you can change the value by invoking `withAsync(boolean async)`
• RocketMQState - For users using Storm Trident API
• TopicSelector - Selecting a topic based on the input Storm tuple
• TupleToMessageMapper - Mapping a Storm tuple to a RocketMQ message, you can implement the
MessageBodySerializer interface to serialize the message body. The default implementation of MessageBodySerializer
is `body.toString().getBytes(StandardCharsets.UTF_8)`
• MessageRetryManager - Retry policy for failed messages
RocketMQ-Spark: https://github.com/apache/rocketmq-externals/tree/master/rocketmq-spark
RocketMQ-Flink: Coming soon
RocketMQ-Avro: Coming soon
OpenMessaging-Streaming