With a current zoo of technologies and different ways of their interaction it's a big challenge to architect a system (or adopt existed one) that will conform to low-latency BigData analysis requirements. Apache Kafka and Kappa Architecture in particular take more and more attention over classic Hadoop-centric technologies stack. New Consumer API put significant boost in this direction. Microservices-based streaming processing and new Kafka Streams tend to be a synergy in BigData world.
3. BigDatain2016+?
● No more an exotic buzzword
● Mature enough and already adopted by majority of businesses/companies
● Set of well-defined tools and processes… questionable
● Data Analysis at scale - taking value from your data!
○ Prescriptive - reveals what action should be taken
○ Predictive - analysis of likely scenarios of what might happen
○ Diagnostic - past analysis, shows what had happened and why (classic)
○ Descriptive - real time analytics (stocks, healthcare..)
4. BigDataanalysischallenges
● Integration - ability to have needed data in needed place
● Latency - data have to be presented for processing immediately
● Throughput - ability to consume/process massive volumes of data
● Consistency - data mutation in one place must be reflected everywhere
● Teams collaboration - inconvenient interface for inter-teams
communication
● Technology adoption - typical technologies stack greatly complicates
entire project ecosystem - another world of hiring, deployment, testing,
scaling, fault tolerance, upgrades, monitoring, etc.
7. Solution
The Event LOG
What every software engineer should know about real-
time data's unifying abstraction
https://engineering.linkedin.com/distributed-systems/log-what-every-
software-engineer-should-know-about-real-time-datas-unifying
12. Kafka
● Fast - single Kafka broker can handle hundreds of megabytes of reads
and writes per second from thousands of clients
● Scalable - can be elastically and transparently expanded without
downtime
● Durable - Messages are persisted on disk and replicated within the
cluster to prevent data loss. Each broker can handle terabytes of
messages without performance impact
● Reliable - has a modern cluster-centric design that offers strong
durability and fault-tolerance guarantees
14. Kafka-buildingblocks
● Producer - process that publishes messages to a Kafka
● Topic - a category or feed name to which messages are published. For
each topic, the Kafka cluster maintains a partitioned log
● Partition - part of a topic: level of parallelism in Kafka. Write/Read order
is guaranteed at partition level
15. Kafka-buildingblocks
● Producer - process that publishes messages to a Kafka
● Topic - a category or feed name to which messages are published. For
each topic, the Kafka cluster maintains a partitioned log
● Partition - part of a topic: level of parallelism in Kafka. Write/Read order
is guaranteed at partition level
● Replica - up-to-date partition’s copy. Each partition is replicated across a
configurable number of servers for fault tolerance (like HDFS block)
16. Kafka-buildingblocks
● Producer - process that publishes messages to a Kafka
● Topic - a category or feed name to which messages are published. For
each topic, the Kafka cluster maintains a partitioned log
● Partition - part of a topic: level of parallelism in Kafka. Write/Read order
is guaranteed at partition level
● Replica - up-to-date partition’s copy. Each partition is replicated across a
configurable number of servers for fault tolerance (like HDFS block)
● Consumer - process that subscribes to topics and processes published
messages
20. StreamProcessing-possibleimplementationframeworks
● Pros
○ Automatic fault tolerance
○ Scaling
○ No data loss guarantees
○ Stream processing DSL/SQL (joins, filters, count aggregates, etc)
● Cons
○ Overall system complexity significantly grows
■ New cluster to maintain/monitor/upgrade/etc (Apache Storm)
■ Multi-pattern (mixed) data access (Spark/Samza on YARN)
○ Another framework to learn for your team
22. StreamProcessing-microservices
Small, independent processes that communicate with each other to form
complex applications which utilize language-agnostic APIs.
These services are small building blocks, highly decoupled and focused on
doing a small task, facilitating a modular approach to system-building.
The microservices architectural style is becoming the standard for building
modern applications.
23. StreamProcessing-microservicescommunication
Three most commonly used protocols are :
● Synchronous request-response calls (mainly via HTTP REST API)
● Asynchronous (non blocking IO) request-response communication (Akka,
Play Framework, etc)
● Asynchronous messages buffers (RabbitMQ, JMS, ActiveMQ, etc)
25. StreamProcessing-microservices
Why can’t I just package
and deploy my events
processing code on Yarn /
Mesos / Docker / Amazon
cluster and let it take care o
fault tolerance, scaling and
other weird things?
29. StreamProcessing-kafka
● New Kafka Consumer 0.9.+
○ Light - consumer client is just a thin JAR without heavy 3rd party
dependencies (ZooKeeper, scala runtime, etc)
○ Acts as Load Balancer
○ Fault tolerant
○ Simple to use API
○ Kafka Streams - elegant DSL (should be officially released this
month)
31. StreamProcessing-kafkaµservices
1. Language agnostic logs of events (buffers)
2. No backpressure on consumers (API endpoints with sync approach)
3. Fault tolerance - no data loss
4. Failed service doesn’t bring entire chain down
5. Resuming from last committed offset position
6. No circuit breaker like patterns needed
7. Smooth configs management across all nodes and services
36. Architecturescomparison
Lambda Kappa
Processing paradigm Batch + Streaming Streaming
Re-processing paradigm Every batch cycles Only when code changes
Resource consumption Higher Lower
Maintenance/Support
complexity
Higher Lower
Ability to re-create dataset
Per any point of time
No (or very hard) Yes
37.
38. Evenmoreinterestingcomparison
Hadoop-centric system Kafka-centric system
Data Replication + +
Fault Tolerance + +
Scaling + +
Random Reads With HBase With Elasticsearch/Solr
Ordered Reads - +
Secondary indices With Elasticsearch/Solr With Elasticsearch/Solr
Storage for Big Files (>10M) + -
TCO higher lower
40. Summary
1. Events Log centric system design - from chaos to structured
architecture
2. Kafka as an Events Log reference storage implementation
41. Summary
1. Events Log centric system design - from chaos to structured
architecture
2. Kafka as an Events Log reference storage implementation
3. Microservices as distributed events processing approach
42. Summary
1. Events Log centric system design - from chaos to structured
architecture
2. Kafka as an Events Log reference storage implementation
3. Microservices as distributed events processing approach
4. Kappa Architecture as Microservices & Kafka symbiosis
43. Usefullinks
1. “I heart Logs” by Jay Krepps http://shop.oreilly.
com/product/0636920034339.do
2. http://confluent.io/blog
3. https://engineering.linkedin.com/distributed-systems/log-what-every-
software-engineer-should-know-about-real-time-datas-unifying
4. “Making sense of stream processing” by Martin Kleppmann
5. http://kafka.apache.org/
6. http://martinfowler.com/articles/microservices.html
