Intro to Apache Apex - Next Gen Platform for Ingest and Transform

Editor's Notes

• #3: Partitioning & Scaling built-in Operators can be dynamically scaled Throughput, latency or any custom logic Streams can be split in flexible ways Tuple hashcode, tuple field or custom logic Parallel partitioning for parallel pipelines MxN partitioning for generic pipelines Unifier concept for merging results from partitions Helps in handling skew imbalance Advanced Windowing support Application window configurable per operator Sliding window and tumbling window support Checkpoint window control for fault recovery Windowing does not introduce artificial latency Stateful fault tolerance out of the box Operators recover automatically from a precise point before failure At least once At most once Exactly once at window boundaries
• #4: In-memory stream processing platform Developed since 2012, ASF TLP since 04/2016 Unobtrusive Java API to express (custom) logic Scale out, distributed, parallel High throughput & low latency processing Windowing (temporal boundary) Reliability, fault tolerance, operability Hadoop native Compute locality, affinity Dynamic updates, elasticity
• #10: Large amount of data to process, arrival at high velocity Pipelining and partitioning Backpressure Partitioning Run same logic in multiple processes or threads Each partition processes a subset of the data Apex supports partitioning out of the box Different partitioning schemes Unification Static & Dynamic Partitioning Separation of processing logic from scaling decisions
• #12: Partitioning decision (yes/no) by trigger (StatsListener) Pluggable component, can use any system or custom metric Externally driven partitioning example: KafkaInputOperator Stateful! Uses checkpointed state Ability to transfer state from old to new partitions (partitioner, customizable) Steps: Call partitioner Modify physical plan, rewrite checkpoints as needed Undeploy old partitions from execution layer Release/request container resources Deploy new partitions (from rewritten checkpoint) No loss of data (buffered) Incremental operation, partitions that don’t change continue processing API: Partitioner interface
• #20: Creating an application in Java provides a much greater flexibility as you can create and use the operators in the same place, two ways Compositional API is low level API where you specify the individual operator implementations and connect them up in a DAG or a graph. Talk about example above. Declarative API where you specify operations on data at a high level and the ApexStream API takes care of converting it into a DAG underneath. Talk about example above.
• #21: For next version we are working on support for Apache Beam and the different even time windowing options in the specification This is the previous example modified with windowing support. There is one global window as we are going to continue to accumulate the counts We want the results every second and we want to keep the counts in state
• #22: With Apex you have the flexibility to create your own custom operators and be able to use it with both low level and high level API. Here are two examples
• #23: A snaphot of our apex malhar library listing the commonly needed operators. Talk about robust support for kafka, dynamic partitioning, 0.8 and 0.9 API support with offset management and idempotent recovery. Operators for streaming and batch
• #24: When your application is running you would want to know what is going on with your application. That is where monitoring console comes in. Touch upon a few salient features Gives you a detailed look into your application including all operators and their partitions. Gives you performance statistics, resource usage and container information for each of the partitions Helps development and operations by providing access to the individual logs right in the console and allowing users to search the logs or change log levels on the fly. Also lets you record data in a stream live at any stage. Talk about locality here quickly By default operators are deployed in containers (processes) on different nodes across the Hadoop cluster Locality options for streams RACK_LOCAL: Data does not traverse network switches NODE_LOCAL: Data transfer via loopback interface, frees up network bandwidth CONTAINER_LOCAL: Data transfer via in memory queues between operators, does not require serialization THREAD_LOCAL: Data passed through call stack, operators share thread Host Locality Operators can be deployed on specific hosts New in 3.4.0: (Anti-)Affinity (APEXCORE-10) Ability to express relative deployment without specifying a host
• #25: Use the built-in real-time dashboards and widgets in your application or connect to your own. This picture shows the variety of widgets we have.
• #26: Wanted to talk about some instances where Apache Apex and DataTorrent are being used in production today. These cases mentioned here, customers have talked about using Apache Apex openly and have presented them in meetups. If you want to know more in depth we have provided links to the those meetup resources at the end. Pubmatic is in advertising space and provides real time analytics around ad impressions and clicks for publishers. They are using dimensional computation operators provided by datatorrent to slice and dice the data in different ways and provide the results of those analytics through real-time dashboards.
• #27: GE is the leader in Industrial IoT and has built a cloud platform called Predix to store and analyze machine data from all over the world. There are using datatorrent and apache apex for high speed ingestion and processing in a fault tolerant way.
• #28: Silver spring networks also in the IoT space provides technology to collect and analyze data from smart energy meters for utilities. They perform ingestion and analytics with datatorrent to detect failures in the systems in advance and reduce outages.