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Reactive Stream Processing with Mantis

This document discusses Mantis, a reactive stream processing system for operational insights. Mantis allows querying data on-demand, reusing data and results between jobs for efficiency. It enables job chaining through discovery of job outputs and auto-scales jobs and clusters based on workload. Mantis provides high throughput and low latency stream processing while maintaining data guarantees.

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Reactive Stream Processing With Mantis Neeraj Joshi - Senior Software Engineer, Edge Systems Nick Mahilani - Senior Software Engineer, Edge Systems October 4, Reactive Summit 2016
Monitoring a complex distributed service @ scale is hard
81+ Million Subscribers Across the Globe
Streaming thousands of Titles
Over Millions of Devices
Powered by 100s of Micro-services
Combinatorial Explosion of Data !!!
Complexity and Comprehension
So, in order to manage complex environments, need to rethink insights, shift the curve
An Insight system that can... Auto-detect anomalies in high volume, high cardinality data
An Insight system that can... Auto-detect anomalies in high volume, high cardinality data Identify titles that have an abnormal failure rate and highlight their common characteristics (only on certain devices using certain CDNs etc)
An Insight system that can... Aggregate rich data On-demand
An Insight system that can... Aggregate rich data On-demand Calculate latency percentiles for PS4 in UK using firmware v1.0 and ui 1.2.1 On Demand
An Insight system that can... Find your needle-in-the-haystack in real-time
An Insight system that can... Find your needle-in-the-haystack in real-time Find me all requests for customer X with latency > 1 seconds
And at the same time be cost effective!
Edge servers alone generate 10 Tb/s of operational data!!
How can we contain the cost?
Reduce Data Two Strategies Optimize Resource Usage
What if? We only stream what is needed & when it is needed?
Do we really need all the data all the time?
Anomaly Detection Use-case Look for abnormal trends in aggregate signal
Anomaly Detection Use-case Look for abnormal trends in aggregate signal Deeper analysis on filtered events
Anomaly Detection Use-case Look for abnormal trends in aggregate signal Deeper analysis on filtered events Aggregate data / filtered data ⇒ Subset of data
Dynamic Dashboards use-case Subset of data
Only subset of fields required Dynamic Dashboards use-case
Aggregate data Only On-demand Dynamic Dashboards use-case
Ad-hoc Realtime Search use-case Looking for a tiny subset of data
What If ? We only stream what is needed & when it is needed? Reuse the data already streamed?
Does every consumer really need different data?
EdgeServers Can we reuse Data? Device Events Q
EdgeServers Device Health Dashboard Realtime Data Aggregator Job All Device Events Can we reuse Data? Device Events Q
EdgeServers Device Health Dashboard Realtime Data Aggregator Job All Device Events Can we reuse Data? Device Events Q Ad-hoc Query Search for device1 events Job All Device Events Device != “device1”
EdgeServers Device Health Dashboard Realtime Data Aggregator Job All Device Events Anomaly Detection Job Alerts All Device Events Can we reuse Data? Device Events Q Ad-hoc Query Search for device1 events Job All Device Events Device != “device1”
EdgeServers Device Health Dashboard Realtime Data Aggregator Job All Device Events Anomaly Detection Job Alerts All Device Events 3x fan out Can we reuse Data? Device Events Q Ad-hoc Query Search for device1 events Job All Device Events Device != “device1”
EdgeServers Device Health Dashboard Realtime Data Aggregator Job All Device Events Anomaly Detection Job Alerts All Device Events 3x fan out Can we reuse Data? Device Events Q Ad-hoc Query Search for device1 events Job All Device Events Device != “device1” Queryable Events Job
EdgeServers Device Health Dashboard Realtime Data Aggregator Job All Device Events Anomaly Detection Job Alerts All Device Events 3x fan out Can we reuse Data? Device Events Q Ad-hoc Query Search for device1 events Job All Device Events Device != “device1” Queryable Events Job (Select status Where true) Only get “projected” events
EdgeServers Device Health Dashboard Realtime Data Aggregator Job All Device Events Anomaly Detection Job Alerts All Device Events 3x fan out Can we reuse Data? Device Events Q Ad-hoc Query Search for device1 events Job All Device Events Device != “device1” Queryable Events Job (Select status Where true) Only get “projected” events
EdgeServers Device Health Dashboard Realtime Data Aggregator Job All Device Events Anomaly Detection Job Alerts All Device Events 3x fan out Can we reuse Data? Device Events Q Ad-hoc Query Search for device1 events Job Queryable Events Job
EdgeServers Device Health Dashboard Realtime Data Aggregator Job All Device Events Anomaly Detection Job Alerts All Device Events 3x fan out Can we reuse Data? Device Events Q Ad-hoc Query Search for device1 events Job Queryable Events Job (select * where device == “device1”)
EdgeServers Device Health Dashboard Realtime Data Aggregator Job All Device Events Anomaly Detection Job Alerts All Device Events 3x fan out Can we reuse Data? Device Events Q Ad-hoc Query Search for device1 events Job Queryable Events Job Only get “filtered” events
EdgeServers Device Health Dashboard Realtime Data Aggregator Job All Device Events Anomaly Detection Job Alerts All Device Events 2x fan out Can we reuse Data? Device Events Q Ad-hoc Query Search for device1 events Job Queryable Events Job Only get “filtered” events
What If ? Only stream what is needed & when it is needed? Reuse the data already streamed? Reuse the results?
EdgeServers Device Health Dashboard Realtime Data Aggregator Job All Device Events 2x fan out Can we reuse Results? Device Events Q Ad-hoc Query Search for device1 events Job Anomaly Detection Job Alerts Queryable Events Job All Device Events
EdgeServers Device Health Dashboard Realtime Data Aggregator Job All Device Events 2x fan out Can we reuse Results? Device Events Q Ad-hoc Query Search for device1 events Job Anomaly Detection Job Alerts Queryable Events Job
EdgeServers Device Health Dashboard Realtime Data Aggregator Job All Device Events 2x fan out Can we reuse Results? Device Events Q Ad-hoc Query Search for device1 events Job Anomaly Detection Job Alerts Queryable Events Job Reuse results
EdgeServers Device Health Dashboard Realtime Data Aggregator Job All Device Events 1x fan out Can we reuse Results? Device Events Q Ad-hoc Query Search for device1 events Job Anomaly Detection Job Alerts Queryable Events Job Reuse results
EdgeServers Device Health Dashboard Realtime Data Aggregator Job All Device Events 1x fan out Streaming Micro-services Device Events Q Ad-hoc Query Search for device1 events Job Anomaly Detection Job Alerts Queryable Events Job Smells like Micro-Services!
What If ? Only stream what is needed & when it is needed? Reuse the data already streamed? Auto-scale resources? Reuse the results?
Do we really need peak resources all the time?
Number of active jobs are unpredictable ActiveJobs Increased activity during incidents
Data volume varies by time of day We see 5 times more data at peak
Job Resources scale with data Data volume Resources used
Mantis ● Small but extremely fast shrimp ● A Reactive stream processing system
Mantis Only stream what is needed & when it is needed Reuse the data & results? Auto-scale resources?
Mantis Only stream what is needed & when it is needed Reuse the data & results? Auto-scale resources? Query based On-demand streaming of data
Mantis Only stream what is needed & when it is needed Reuse the data & results? Auto-scale resources? Query based On-demand streaming of data Built-in Job Discovery and Job Chaining
Mantis Only stream what is needed & when it is needed Reuse the data & results? Auto-scale resources? Query based On-demand streaming of data Built-in Job Discovery and Job Chaining Job and Cluster Auto-scaling
+ Much More ● High throughput, low latency stream processing system focused on Operational Insights ● Configurable data guarantees ● Long running & Transient jobs ● Flexible Functional programming with RxJava
Mantis deep-dive ● Query based On-demand Streaming of data ● Job Discovery and Job Chaining ● Auto-scaling Jobs and Clusters ● End-to-end Reactive Stream Semantics
Mantis Architecture Mesos Framework Fenzo Scheduler Mantis Master Mantis Agents EC2 instance EC2 instance EC2 instance Mantis Job code runs in Containers
Mantis Architecture Mesos Framework Fenzo Scheduler Mantis Master Mantis Agents EC2 instance EC2 instance EC2 instance Mantis API Mantis Job code runs in Containers Mantis UI
Mantis Job ● Source ○ Observable< Observable<T> > ● 1…N Stages ○ Observable<T> → Observable<R> ● Sink ○ Observable<R>
Mantis ● Query based On-demand Streaming of data ● Job Discovery and Job Chaining ● Auto-scaling Jobs and Clusters ● End-to-end Reactive Stream Semantics
Query Based On Demand Streaming ● Stream data only when needed and only what is needed ● Filter data at the source ● Cleanup after use Data Source QueryRequested Data Mantis Job
Mantis Query Language (MQL) SELECT xid, errorCode WHERE device-type == SONY_PS3 SAMPLE {"strategy": "RANDOM", "threshold": 200} Projection Filtering Sampling
Query processing on Data producing app API MRE Mantis Real-time Events library
Query processing on Data producing app API MRE QoE Analysis Mantis Job Mantis Real-time Events library SELECT xid WHERE type = rebuffer
Query processing on Data producing app API MRE QoE Analysis Mantis Job SELECT xid WHERE type = rebuffer Mantis Real-time Events library { “xid”: 1234}, { “xid”: 4567}
Query processing on Data producing app API MRE QoE Analysis Mantis Job SELECT xid WHERE type = rebuffer Mantis Real-time Events library { “xid”: 1234}, { “xid”: 4567} Device Analysis Mantis Job SELECT * WHERE device = XBox { “device”: “XBox”, “IP”: 1.1.1.1, “xid”:1111 }
Mantis ● Query based On-demand Streaming of data ● Job Discovery and Job Chaining ● Auto-scaling Jobs and Clusters ● End-to-end Reactive Stream Semantics
Job Discovery & Chaining Aggregator Job Worker 1 Aggregator Job Worker 2 Anomaly Job Worker 1 Anomaly Job Worker 2 Mantis Master
Job Discovery & Chaining Aggregator Job Worker 1 Aggregator Job Worker 2 Anomaly Job Worker 1 Anomaly Job Worker 2 Mantis Master Subscribe to Aggregator Job scheduling info stream
Job Discovery & Chaining Aggregator Job Worker 1 Aggregator Job Worker 2 Anomaly Job Worker 1 Anomaly Job Worker 2 Aggregator Job scheduling info { worker1 : { host : 1.1.1.1, port : 8888 }, … } Mantis Master
Job Discovery & Chaining Aggregator Job Worker 1 Aggregator Job Worker 2 Anomaly Job Worker 1 Anomaly Job Worker 2 Connect with Mantis Query Mantis Master
Job Discovery & Chaining Aggregator Job Worker 1 Aggregator Job Worker 2 Anomaly Job Worker 1 Anomaly Job Worker 2 Filtered data Mantis Master
Aggregator Job Worker 2 Mantis Master Fault tolerance: Worker failure Aggregator Job Worker 1 Anomaly Job Worker 1 Anomaly Job Worker 2 Filtered data
Aggregator Job Worker 2 Mantis Master Fault tolerance: Worker failure Aggregator Job Worker 1 Anomaly Job Worker 1 Anomaly Job Worker 2 Filtered data
Aggregator Job Worker 2 Fault tolerance: Worker failure Aggregator Job Worker 1 Anomaly Job Worker 1 Anomaly Job Worker 2 Aggregator Job Worker 3 Mantis Master
Aggregator Job Worker 2 Fault tolerance: Worker failure Aggregator Job Worker 1 Anomaly Job Worker 1 Anomaly Job Worker 2 Mantis Master Aggregator Job scheduling info Aggregator Job Worker 3 Mantis Master
Aggregator Job Worker 2 Fault tolerance: Worker failure Aggregator Job Worker 1 Anomaly Job Worker 1 Anomaly Job Worker 2 Aggregator Job Worker 3 Filtered data Mantis Master
Mantis Master Aggregator v1 Worker 2 In Service Job updates Aggregator v1 Worker 1 Anomaly Job Worker 1 Anomaly Job Worker 2 Filtered data Aggregator v2 Worker 2 Aggregator v2 Worker 1
Aggregator v1 Worker 2 In Service Job updates Aggregator v1 Worker 1 Anomaly Job Worker 1 Anomaly Job Worker 2 Mantis Master Filtered data Aggregator v2 Worker 2 Aggregator v2 Worker 1 Aggregator v2 scheduling info Mantis Master
Aggregator v1 Worker 2 In Service Job updates Aggregator v1 Worker 1 Anomaly Job Worker 1 Anomaly Job Worker 2 Connect with Mantis Query Aggregator v2 Worker 2 Aggregator v2 Worker 1 Mantis Master
Aggregator v1 Worker 2 In Service Job updates Aggregator v1 Worker 1 Anomaly Job Worker 1 Anomaly Job Worker 2 Filtered data Aggregator v2 Worker 2 Aggregator v2 Worker 1 Mantis Master
Job Chaining Example
Kafka partition - multiple consumers 0 N Kafka TopicPartition Consumer 1 (device == XBox) Consumer 2 (type == rebuffer) Consumer 3 (xid = 0afcedbxe)
Reuse Kafka Data Streams 0 N Mantis Kafka Consumer Job Kafka TopicPartition
Reuse Kafka Data Streams 0 N SELECT * WHERE device = XBox SELECT * WHERE xid = 0afcedbxe SELECT * WHERE type = re-buffer Mantis Kafka Consumer Job Device Analysis Job QoE analysis Job Adhoc Transaction Analysis Job Kafka TopicPartition
Mantis ● Query based On-demand Streaming of data ● Job Discovery and Job Chaining ● Auto-scaling Jobs and Clusters ● End-to-end Reactive Stream Semantics
Mantis Agent Cluster Autoscaling EC2 InstanceEC2 Instance Job 1 Workers
Mantis Agent Cluster Autoscaling EC2 InstanceEC2 Instance Job 2 Workers
Mantis Agent Cluster Autoscaling EC2 Instance EC2 InstanceEC2 Instance Job 2 Workers
Mantis Agent Cluster Autoscaling EC2 Instance EC2 InstanceEC2 Instance Job 2 scale up
Mantis Agent Cluster Autoscaling EC2 Instance EC2 InstanceEC2 Instance Job 2 scale up EC2 Instance
Bin Packing ● Simple round robin scheduling causes fragmentation ● Smarter bin-packing of jobs frees up mantis agents for scale down Host 1 Host 2 Host 3 Host 4 v/s Host 1 Host 2 Host 3 Host 4
Mantis ● Query based On-demand Streaming of data ● Job Discovery and Job Chaining ● Auto-scaling Jobs and Clusters ● End-to-end Reactive Stream Semantics
Back Pressure
End-to-end Reactive Streams ● RxJava operators compose backpressure within a single worker ● Reactive Socket for backpressure across network boundaries
● Application layer protocol for async non-blocking backpressure across network boundaries ● Rich interaction modes ● Pluggable transport protocol Reactive Socket Node A Node B Request N Data
End-to-end Reactive Streams filter() map() groupBy Stage 1 data data demand demand Mantis Job
End-to-end Reactive Streams filter() map() groupBy Stage 1 data data demand demand Mantis Job window() reduce() flatmap() Stage 2 data data demand demand Reactive Socket
End-to-end Reactive Streams Stage 1 Reactive Socket Mantis Job 1 Mantis Job 2
Reactive Stream Processing
Reactive Stream Processing Message Driven
Reactive Stream Processing Message Driven Elastic
Reactive Stream Processing Message Driven Elastic Resilient
Reactive Stream Processing Message Driven Elastic Resilient Responsive
Mantis Today ● ~650 Jobs in production ● ~8 Million events / sec processed ● 80 Gb/s processed (instead of 10 Tb/s due to filtering) i.e. 99% less data !! ● The processed data gets reused by other jobs further reducing costs ● Auto-scaling jobs use up to 75% fewer resources compared to peak
References ● Mantis Blogpost http://techblog.netflix.com/2016/03/stream-processing-with-mantis.html ● Resource Scheduling on Mesos https://www.youtube.com/watch?v=uyGEgWAG9EQ ● Fenzo https://github.com/Netflix/Fenzo ● RxJava https://github.com/ReactiveX/RxJava ● Reactive Socket http://reactivesocket.io/ ● RxNetty https://github.com/ReactiveX/RxNetty
Questions? Reactive Stream Processing with Mantis Neeraj Joshi Nick Mahilani @neerajrj @nick_mahilani

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