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Timothy Spann: Apache Pulsar for ML

  1. 1. Apache Pulsar for ML Tim Spann | Developer Advocate
  2. 2. AGENDA ● Apache Pulsar ● ML Functions ● Demo
  3. 3. Tim Spann Developer Advocate Tim Spann Developer Advocate at StreamNative ● FLiP(N) Stack = Flink, Pulsar and NiFi Stack ● Streaming Systems & Data Architecture Expert ● Experience: ○ 15+ years of experience with streaming technologies including Pulsar, Flink, Spark, NiFi, Big Data, Cloud, MXNet, IoT, Python and more. ○ Today, he helps to grow the Pulsar community sharing rich technical knowledge and experience at both global conferences and through individual conversations.
  4. 4. This week in Apache Flink, Apache Pulsar, Apache NiFi, Apache Spark and open source friends. FLiP Stack Weekly
  5. 5. Apache Pulsar
  6. 6. What are the Benefits of Pulsar? Data Durability Scalability Geo-Replication Multi-Tenancy Unified Messaging Model
  7. 7. Apache Pulsar is a Cloud-Native Messaging and Event-Streaming Platform.
  8. 8. CREATED Originally developed inside Yahoo! as Cloud Messaging Service GROWTH 10x Contributors 10MM+ Downloads Ecosystem Expands Kafka on Pulsar AMQP on Pulsar Functions . . . 2012 2016 2018 TODAY APACHE TLP Pulsar becomes Apache top level project. OPEN SOURCE Pulsar committed to open source. Apache Pulsar Timeline
  9. 9. Evolution of Pulsar Growth
  10. 10. Pulsar Has a Built-in Super Set of OSS Features Durability Scalability Geo-Replication Multi-Tenancy Unified Messaging Model Reduced Vendor Dependency Functions Open-Source Features
  11. 11. Apache Pulsar is built to support legacy applications, handle the needs of modern apps, and supports NextGen applications Support legacy workloads. Compatible with popular messaging and streaming tools. Legacy Built for today's real-time event driven applications. Modern Scalable, adaptive architecture ready for the future of real-time streaming. NextGen
  12. 12. Apache Pulsar has a vibrant community 560+ Contributors 10,000+ Commits 7,000+ Slack Members 1,000+ Organizations Using Pulsar
  13. 13. It is often assumed that Pulsar and Kafka have equal capabilities. In reality, Pulsar offers a superset of Kafka. ● Pulsar is streaming and queuing together ● Pulsar is cloud-native with stateless brokers ● Natively includes geo-replication, multi-tenancy, and end-to-end security out of the box ● Pulsar provides automated rebalancing ● Pulsar offers 100X lower latency w/ 2.5 greater throughput than Kafka Advantages of Apache Pulsar
  14. 14. Apache Pulsar features Cloud native with decoupled storage and compute layers. Built-in compatibility with your existing code and messaging infrastructure. Geographic redundancy and high availability included. Centralized cluster management and oversight. Elastic horizontal and vertical scalability. Seamless and instant partitioning rebalancing with no downtime. Flexible subscription model supports a wide array of use cases. Compatible with the tools you use to store, analyze, and process data.
  15. 15. ● “Bookies” ● Stores messages and cursors ● Messages are grouped in segments/ledgers ● A group of bookies form an “ensemble” to store a ledger ● “Brokers” ● Handles message routing and connections ● Stateless, but with caches ● Automatic load-balancing ● Topics are composed of multiple segments ● ● Stores metadata for both Pulsar and BookKeeper ● Service discovery Store Messages Metadata & Service Discovery Metadata & Service Discovery Pulsar Cluster Metadata Storage Pulsar Cluster
  16. 16. Component Description Value / Data payload The data carried by the message. All Pulsar messages contain raw bytes, although message data can also conform to data schemas. Key Messages are optionally tagged with keys, used in partitioning and also is useful for things like topic compaction. Properties An optional key/value map of user-defined properties. Producer name The name of the producer who produces the message. If you do not specify a producer name, the default name is used. Sequence ID Each Pulsar message belongs to an ordered sequence on its topic. The sequence ID of the message is its order in that sequence. Messages - the Basic Unit of Apache Pulsar
  17. 17. Different subscription modes have different semantics: Exclusive/Failover - guaranteed order, single active consumer Shared - multiple active consumers, no order Key_Shared - multiple active consumers, order for given key Producer 1 Producer 2 Pulsar Topic Subscription D Consumer D-1 Consumer D-2 Key-Shared < K 1, V 10 > < K 1, V 11 > < K 1, V 12 > < K 2 ,V 2 0 > < K 2 ,V 2 1> < K 2 ,V 2 2 > Subscription C Consumer C-1 Consumer C-2 Shared < K 1, V 10 > < K 2 ,V 2 1> < K 1, V 12 > < K 2 ,V 2 0 > < K 1, V 11 > < K 2 ,V 2 2 > Subscription A Consumer A Exclusive Subscription B Consumer B-1 Consumer B-2 In case of failure in Consumer B-1 Failover Apache Pulsar Subscription Modes
  18. 18. Streaming Consumer Consumer Consumer Subscription Shared Failover Consumer Consumer Subscription In case of failure in Consumer B-0 Consumer Consumer Subscription Exclusive X Consumer Consumer Key-Shared Subscription Pulsar Topic/Partition Messaging
  19. 19. Unified Messaging Model Simplify your data infrastructure and enable new use cases with queuing and streaming capabilities in one platform. Multi-tenancy Enable multiple user groups to share the same cluster, either via access control, or in entirely different namespaces. Scalability Decoupled data computing and storage enable horizontal scaling to handle data scale and management complexity. Geo-replication Support for multi-datacenter replication with both asynchronous and synchronous replication for built-in disaster recovery. Tiered storage Enable historical data to be offloaded to cloud-native storage and store event streams for indefinite periods of time. Apache Pulsar Benefits
  20. 20. Messaging Use Cases Streaming Use Cases Service x commands service y to make some change. Example: order service removing item from inventory service Moving large amounts of data to another service (real-time ETL). Example: logs to elasticsearch Distributing messages that represent work among n workers. Example: order processing not in main “thread” Periodic jobs moving large amounts of data and aggregating to more traditional stores. Example: logs to s3 Sending “scheduled” messages. Example: notification service for marketing emails or push notifications Computing a near real-time aggregate of a message stream, split among n workers, with order being important. Example: real-time analytics over page views Messaging vs Streaming
  21. 21. Messaging Use Case Streaming Use Case Retention The amount of data retained is relatively small - typically only a day or two of data at most. Large amounts of data are retained, with higher ingest volumes and longer retention periods. Throughput Messaging systems are not designed to manage big “catch-up” reads. Streaming systems are designed to scale and can handle use cases such as catch-up reads. Differences in Consumption
  22. 22. byte[] msgIdBytes = // Some byte array MessageId id = MessageId.fromByteArray(msgIdBytes); Reader<byte[]> reader = pulsarClient.newReader() .topic(topic) .startMessageId(id) .create(); Create a reader that will read from some message between earliest and latest. Reader Apache Pulsar Reader Interface
  23. 23. ● New Consumer type added in Pulsar 2.10 that provides a continuously updated key-value map view of compacted topic data. ● An abstraction of a changelog stream from a primary-keyed table, where each record in the changelog stream is an update on the primary-keyed table with the record key as the primary key. ● READ ONLY DATA STRUCTURE! Apache Pulsar TableView
  24. 24. Schema Registry schema-1 (value=Avro/Protobuf/JSON) schema-2 (value=Avro/Protobuf/JSON) schema-3 (value=Avro/Protobuf/JSON) Schema Data ID Local Cache for Schemas + Schema Data ID + Local Cache for Schemas Send schema-1 (value=Avro/Protobuf/JSON) data serialized per schema ID Send (register) schema (if not in local cache) Read schema-1 (value=Avro/Protobuf/JSON) data deserialized per schema ID Get schema by ID (if not in local cache) Producers Consumers Schema Registry
  25. 25. ● Utilizing JSON Data with a JSON Schema ● Consistency, Contracts, Clean Data ● This enables easy SQL: ○ Pulsar SQL (Presto SQL) ○ Flink SQL ○ Spark Structured Streaming Use Schemas
  26. 26. • Functions - Lightweight Stream Processing (Java, Python, Go) • Connectors - Sources & Sinks (Cassandra, Kafka, …) • Protocol Handlers - AoP (AMQP), KoP (Kafka), MoP (MQTT) • Processing Engines - Flink, Spark, Presto/Trino via Pulsar SQL • Data Offloaders - Tiered Storage - (S3) Sources, Sinks and Processing
  27. 27. Kafka on Pulsar (KoP)
  28. 28. MQTT on Pulsar (MoP)
  29. 29. AMQP on Pulsar (AoP)
  30. 30. Use Apache Pulsar For Ingest
  31. 31. Use Apache Pulsar To Stream to Lakehouses
  32. 32. Pulsar SQL Presto/Trino workers can read segments directly from bookies (or offloaded storage) in parallel. Bookie 1 Segment 1 Producer Consumer Broker 1 Topic1-Part1 Broker 2 Topic1-Part2 Broker 3 Topic1-Part3 Segment 2 Segment 3 Segment 4 Segment X Segment 1 Segment 1 Segment 1 Segment 3 Segment 3 Segment 3 Segment 2 Segment 2 Segment 2 Segment 4 Segment 4 Segment 4 Segment X Segment X Segment X Bookie 2 Bookie 3 Query Coordinator ... ... SQL Worker SQL Worker SQL Worker SQL Worker Query Topic Metadata
  33. 33. ● Event-Driven services that are interested in events from other services can use Pulsar subscriptions to receive new events in the stream. ● Pulsar supports both linear (exclusive) and filtered subscriptions (key-shared). ● You can also use StreamNative’s filtering & routing service to provide filtered subscriptions. Subscriptions
  34. 34. Functions
  35. 35. ● Visual Question and Answer ● Natural Language Processing ● Sentiment Analysis ● Text Classification ● Named Entity Recognition ● Content-based Recommendations • Predictive Maintenance • Fault Detection • Fraud Detection • Time-Series Predictions • Naive Bayes Apache Pulsar Functions for ML Models
  36. 36. Benefits of Pulsar Functions • Allows you to focus on the business logic. • Sets up producer/consumer for you, eliminating the need for boilerplate code. • Handles message consumption and publication for you. • Run as threads, processes or Kubernetes Pods inside Pulsar. No need for another processing framework. Can scale up automatically.
  37. 37. Event-Based Microservices Application • A basic order entry use case for a food delivery service will involve several different microservices. • The communicate via messages sent to Pulsar topics. • A service can also subscribe to the events published by another service.
  38. 38. Function Mesh Pulsar Functions, along with Pulsar IO/Connectors, provide a powerful API for ingesting, transforming, and outputting data. Function Mesh, another StreamNative project, makes it easier for developers to create entire applications built from sources, functions, and sinks all through a declarative API.
  39. 39. ML • Visual Question and Answer • NLP (Natural Language Processing) • Sentiment Analysis • Text Classification • Named Entity Recognition • Content-based Recommendations • Predictive Maintenance • Fault Detection • Fraud Detection • Time-Series Predictions • Naive Bayes
  40. 40. Why Pulsar Functions for Microservices? Desired Characteristic Pulsar Functions… Highly maintainable and testable Are small pieces of code written in popular languages such as Java, Python, or Go. They can be easily maintained in source control repositories and tested with existing frameworks automatically. Loosely coupled with other services Are not directly linked to one another and communicate via messages. Independently deployable Are designed to be deployed independently Can be developed by a small team Are often developed by a single developer. Inter-service Communication Support all message patterns using Pulsar as the underlying message bus. Deployment & Composition Can run as individual threads, processes, or K8s pods. The Function Mesh allows you to deploy multiple Pulsar Functions as a single unit.
  41. 41. Built-in Back Pressure Producer<String> producer = client.newProducer(Schema.STRING) .topic("hellotopic") .blockIfQueueFull(true) // enable blocking // when queue is full .maxPendingMessages(10) // max queue size .create(); // During Back Pressure: the sendAsync call blocks // with no room in queues producer.newMessage() .key("mykey") .value("myvalue") .sendAsync(); // can be a blocking call
  42. 42. ML Java Coding (Deep Java Library)
  43. 43. ● Lightweight computation similar to AWS Lambda. ● Specifically designed to use Apache Pulsar as a message bus. ● Function runtime can be located within Pulsar Broker. ● Java Functions A serverless event streaming framework Pulsar Functions
  44. 44. ● Consume messages from one or more Pulsar topics. ● Apply user-supplied processing logic to each message. ● Publish the results of the computation to another topic. ● Support multiple programming languages (Java, Python, Go) ● Can leverage 3rd-party libraries to support the execution of ML models on the edge. Pulsar Functions
  45. 45. Simple Native Java Function
  46. 46. Simple Function with Pulsar SDK
  47. 47. from pulsar import Function from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer import json class Chat(Function): def __init__(self): pass def process(self, input, context): fields = json.loads(input) sid = SentimentIntensityAnalyzer() ss = sid.polarity_scores(fields["comment"]) row = { } row['id'] = str(msg_id) if ss['compound'] < 0.00: row['sentiment'] = 'Negative' else: row['sentiment'] = 'Positive' row['comment'] = str(fields["comment"]) json_string = json.dumps(row) return json_string Entire Function Pulsar Python NLP Function
  48. 48. Starting a Function - Distributed Cluster Once compiled into a JAR, start a Pulsar Function in a distributed cluster:
  49. 49. Pulsar Functions ● Route ● Enrich ● Convert ● Lookups ● Run Machine Learning ● Logging ● Auditing ● Parse ● Split ● Convert
  50. 50. Pulsar Functions ● Consume messages from one or more Pulsar topics. ● Apply user-supplied processing logic to each message. ● Publish the results of the computation to another topic. ● Support multiple programming languages (Java, Python, Go) ● Can leverage 3rd-party libraries to support the execution of ML models on the edge.
  51. 51. Building Real-Time Apps Requires a Team
  52. 52. NLP Streaming Architecture
  53. 53. Learn More
  54. 54. Deploying AI With an Event-Driven Platform
  55. 55. Apache Pulsar in Action Please enjoy David’s complete book which is the ultimate guide to Pulsar.
  56. 56. Now Available On-Demand Pulsar Training
  57. 57.
  58. 58. Github ● ● ● ● ● ● ● ● ●
  59. 59. Scan the QR code to learn more about Apache Pulsar and StreamNative.
  60. 60. Scan the QR code to build your own apps today.
  61. 61. Tim Spann Developer Advocate @PaaSDev Let’s Keep in Touch