iot ingestion & analytics using apache apex - a native hadoop platform
TRANSCRIPT
Pramod Immaneni <[email protected]>PPMC Member, Architect @DataTorrent IncApr 6th, 2016
A native Hadoop platform
IoT Ingestion & Analytics using Apache Apex
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IoT (Internet of Things)• Network of objects in physical world with electronic sensors
• Collecting and sending data•Consumer
• Smart home – Smart bulbs, thermostats, locks• Connected vehicles • Wearables
• Industrial• Manufacturing• Retail• Energy
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Challenges
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• Velocity of data• Machines generate data at a much higher pace than humans• They generate data continuously without pause posing
processing challenges• Certain aspects of data may become stale if not processed in
time• Volume
• Large volumes of data for processing and storage• Volume keep increasing because of the exponential growth in
devices• Research estimates zettabytes by 2020
• Variety• Types of data – sensor, location, kpi • Formats – csv, json• Delivery mechanisms – files, message queues
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What should a solution have
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• Ingest and perform analytics on data in motion• Handle the incoming throughput while maintaining SLAs
• Scale with the volume of data• Fault tolerant so that no data is lost• Flexible to handle data variety and allow custom business logic
• Easy to use tools for the user
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What is Apex• Platform and runtime engine to enable scalable and fault-tolerant distributed applications
• Hadoop native• Process streaming or batch big data• High throughput and low latency• Library of commonly needed business logic• Build any custom business logic in your application
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Applications• Distributed processing
• Application logic broken into components called operators that run in a distributed fashion across your cluster
• Scalable• Operators can be partitioned into multiple instances to handle large volumes
• Fault tolerant• Automatically recover from node outages without having to reprocess from
beginning• State is preserved• Long running applications
• Operators• Use library to build applications quickly• Write your own in Java using the API
• Operational insight – DataTorrent RTS• See how each operator is performing and even record data
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Apex Platform Overview
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Apache Malhar Library
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Application Development Model
A Stream is a sequence of data tuplesA typical Operator takes one or more input streams, performs computations & emits one or more output streams
• Each Operator is YOUR custom business logic in java, or built-in operator from our open source library• Operator has many instances that run in parallel and each instance is single-threaded
Directed Acyclic Graph (DAG) is made up of operators and streams
Directed Acyclic Graph (DAG)
Filtered
Stream
Output StreamTuple Tuple
Filtered Stream
Enriched Stream
Enriched
Stream
er
Operator
er
Operator
er
Operator
er
Operator
er
Operator
er
Operator
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Native Hadoop Integration
• YARN is the resource manager
• HDFS used for storing any persistent state
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Advanced Windowing Support
Application window Sliding window and tumbling window
Checkpoint window No artificial latency
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Scalability & PartitioningNxM PartitionsUnifier
0 1 2 3
Logical DAG
0 1 2
1
1 Unifier
1
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Logical Diagram
Physical Diagram with operator 1 with 3 partitions
0
Unifier
1a
1b
1c
2a
2b
Unifier 3
Physical DAG with (1a, 1b, 1c) and (2a, 2b): No bottleneck
Unifier
Unifier0
1a
1b
1c
2a
2b
Unifier 3
Physical DAG with (1a, 1b, 1c) and (2a, 2b): Bottleneck on intermediate Unifier
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Advanced Partitioning
0
1a
1b
2 3 4Unifier
Physical DAG
0 4
3a2a1a
1b 2b 3b
Unifier
Physical DAG with Parallel Partition
Parallel Partition
Container
uopr
uopr1
uopr2
uopr3
uopr4
uopr1
uopr2
uopr3
uopr4
dopr
dopr
doprunifier
unifier
unifier
unifier
Container
Container
NIC
NIC
NIC
NIC
NIC
Container
NIC
Logical Plan
Execution Plan, for N = 4; M = 1
Execution Plan, for N = 4; M = 1, K = 2 with cascading unifiers
Cascading Unifiers
0 1 2 3 4
Logical DAG
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Dynamic Partitioning
• Partitioning change while application is runningᵒ Change number of partitions at runtime based on statsᵒ Determine initial number of partitions dynamically
• Kafka operators scale according to number of kafka partitionsᵒ Supports re-distribution of state when number of partitions changeᵒ API for custom scaler or partitioner
2b
2c
3
2a
2d
1b
1a1a 2a
1b 2b
3
1a 2b
1b 2c 3b
2a
2d
3a
Unifiers not shown
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Fault Tolerance• Operator state is checkpointed to a persistent store
ᵒ Automatically performed by engine, no additional work needed by operator
ᵒ In case of failure operators are restarted from checkpoint stateᵒ Frequency configurable per operatorᵒ Asynchronous and distributed by defaultᵒ Default store is HDFS
• Automatic detection and recovery of failed operatorsᵒ Heartbeat mechanism
• Buffering mechanism to ensure replay of data from recovered point so that there is no loss of data
• Application master state checkpointed
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Processing GuaranteesAtleast once• On recovery data will be replayed from a previous checkpoint
ᵒ Messages will not be lostᵒ Default mechanism and is suitable for most applications
• Can be used in conjunction with following mechanisms to achieve exactly-once behavior in fault recovery scenariosᵒ Transactions with meta information, Rewinding output, Feedback
from external entity, Idempotent operationsAtmost once• On recovery the latest data is made available to operator
ᵒ Useful in use cases where some data loss is acceptable and latest data is sufficient
Exactly once• At least once + state recovery + operator logic to achieve
end-to-end exactly once
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Ingestion Operators• NFS/FTP/S3/HDFS Readers and Writers• Kafka readers and writers• Cassandra, HBase, Hive, JDBC• CSV, JSON, Avro, XML parsers• Filtering and enrichment• Dedup• Dimension computation• Stream Joins• …
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Pipeline builder
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Monitoring ConsoleLogical View
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Monitoring ConsolePhysical View
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Real-Time DashboardsReal Time Visualization
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IoT solutions
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Resources• Ingest application – http://www.datatorrent.com/download• Solutions – http://www.datatorrent.com/solutions• Subscribe - http://apex.incubator.apache.org/community.html• Twitter - @ApacheApex; Follow - https://twitter.com/apacheapex• Free Enterprise License for Startups -
https://www.datatorrent.com/product/startup-accelerator/ • Cloud Trial - http://datatorrent.com/cloudtrial.html
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We Are Hiring• [email protected]• Developers/Architects• QA Automation Developers• Information Developers• Build and Release• Community Leaders
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Upcoming Events• April 14th 6pm PDT – IOT Big Data Ingestion and Processing in
Hadoop by Silver Spring Networks• May 4th 6pm PDT – Fault Tolerant HDFS r/w with Apache Apex
and Apex (native Hadoop) benchmarks
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