Webinar: Data Streaming with Apache Kafka & MongoDB
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This document summarizes a webinar about integrating Apache Kafka and MongoDB for data streaming. The webinar covered: - An overview of Apache Kafka and how it can be used for data transport and integration as well as real-time stream processing. - How MongoDB can be used as both a Kafka producer, to stream data into Kafka topics, and as a Kafka consumer, to retrieve streamed data from Kafka for storage, querying, and analytics in MongoDB. - Various use cases for integrating Kafka and MongoDB, including handling real-time updates, storing raw and processed event data, and powering real-time applications with analytics models built from streamed data.
![#MongoDBWebinar | @mongodb
Document Data Model
Relational MongoDB
{ customer_id : 1,
first_name : "Mark",
last_name : "Smith",
city : "San Francisco",
phones: [
{
number : “1-212-777-1212”,
dnc : true,
type : “home”
},
number : “1-212-777-1213”,
type : “cell”
}]
}
Customer ID First Name Last Name City
0 John Doe New York
1 Mark Smith San Francisco
2 Jay Black Newark
3 Meagan White London
4 Edward Daniels Boston
Phone Number Type DNC Customer ID
1-212-555-1212 home T 0
1-212-555-1213 home T 0
1-212-555-1214 cell F 0
1-212-777-1212 home T 1
1-212-777-1213 cell (null) 1
1-212-888-1212 home F 2](https://image.slidesharecdn.com/mongodbandkafkawebinar1-160913185014/85/Webinar-Data-Streaming-with-Apache-Kafka-MongoDB-46-320.jpg)
![#MongoDBWebinar | @mongodb
Document Model Benefits
{
customer_id : 1,
first_name : "Mark",
last_name : "Smith",
city : "San Francisco",
phones: [
{
number : “1-212-777-1212”,
dnc : true,
type : “home”
},
number : “1-212-777-1213”,
type : “cell”
}]
}
Agility and flexibility
Data model supports business change
Rapidly iterate to meet new requirements
Intuitive, natural data representation
Eliminates ORM layer
Developers are more productive
Reduces the need for joins, disk seeks
Programming is more simple
Performance delivered at scale](https://image.slidesharecdn.com/mongodbandkafkawebinar1-160913185014/85/Webinar-Data-Streaming-with-Apache-Kafka-MongoDB-47-320.jpg)
![#MongoDBWebinar | @mongodb
Rich Functionality
MongoDB
Expressive Queries
• Find anyone with phone # “1-212…”
• Check if the person with number “555…” is on the “do not
call” list
Geospatial
• Find the best offer for the customer at geo coordinates of 42nd
St. and 6th
Ave
Text Search • Find all tweets that mention the firm within the last 2 days
Aggregation • Count and sort number of customers by city
Native Binary
JSON support
• Add an additional phone number to Mark Smith’s without
rewriting the document
• Select just the mobile phone number in the list
• Sort on the modified date
{ customer_id : 1,
first_name : "Mark",
last_name : "Smith",
city : "San Francisco",
phones: [ {
number : “1-212-777-1212”,
dnc : true,
type : “home”
},
{
number : “1-212-777-1213”,
type : “cell”
}]
}
Left outer join
($lookup)
• Query for all San Francisco residences,lookup their
transactions, and sum the amount by person](https://image.slidesharecdn.com/mongodbandkafkawebinar1-160913185014/85/Webinar-Data-Streaming-with-Apache-Kafka-MongoDB-48-320.jpg)
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Webinar: Data Streaming with Apache Kafka & MongoDB
- 1. #MongoDBWebinar | @mongodb Data Streaming with Apache Kafka & MongoDB Andrew Morgan –MongoDB Product Marketing David Tucker–Director, Partner Engineering andAlliances atConfluent 13th September 2016
- 2. #MongoDBWebinar | @mongodb Agenda Target Audience Apache Kafka MongoDB Integrating MongoDB and Kafka Kafka – What’s Next Next Steps
- 3. #MongoDBWebinar | @mongodb Target Audience
- 4. #MongoDBWebinar | @mongodb Target Audience
- 5. #MongoDBWebinar | @mongodb Target Audience
- 6. #MongoDBWebinar | @mongodb Target Audience
- 7. #MongoDBWebinar | @mongodb Target Audience
- 8. #MongoDBWebinar | @mongodb Target Audience
- 9. #MongoDBWebinar | @mongodb Apache Kafka / Confluent Platform
- 10. #MongoDBWebinar | @mongodb What does Kafka do? Producers Consumers Kafka Connect Kafka Connect Topic Your interfaces to the world Connected to your systems in real time
- 11. #MongoDBWebinar | @mongodb What is Streaming Data Synchronous Req/Response 0 – 100s ms Near Real Time > 100s ms Offline Batch > 1 hour KAFKA Stream Data Platform Search RDBMS Apps Monitoring Real-time AnalyticsNoSQL Stream Processing HADOOP Data Lake Impala DWH Hive Spark Map-Reduce
- 12. #MongoDBWebinar | @mongodb Confluent’s Offerings Core Connect Streams Java Client Kafka Confluent Platform EnterpriseConfluent Platform Stream MonitoringMore Clients Message DeliveryREST Proxy Stream MonitoringSchema Registry Connector ManagementPre-Built Connectors
- 13. #MongoDBWebinar | @mongodb Confluent Platform: It’s Kafka ++ Feature Benefit Apache Kafka Confluent Platform Confluent Platform Enterprise Apache Kafka High throughput,low latency, high availability, secure distributedmessage system Kafka Connect Advanced framework for connecting external sources/destinations into Kafka Java Client Provides easy integration intoJava applications Kafka Streams Simple library that enables streaming applicationdevelopment within the Kafka framework Additional Clients Supports non-Javaclients; C, C++, Python, etc. REST Proxy Provides universal access to Kafka from any network connected devicevia HTTP Schema Registry Central registry for the format of Kafka data – guarantees all data is always consumable Pre-Built Connectors HDFS, JDBC and other connectors fully Certified and fully supported by Confluent Confluent Control Center Includes Connector Managementand Stream Monitoring Support Enterprise class support to keep your Kafkaenvironment running at top performance Community Community 24x7x365 Free Free Subscription
- 14. #MongoDBWebinar | @mongodb Common Kafka Use Cases Data transport and integration • Log data • Database changes • Sensors and device data • Monitoring streams • Call data records • Stock ticker data Real-time stream processing • Monitoring • Asynchronous applications • Fraud and security
- 15. #MongoDBWebinar | @mongodb People Using Kafka Today Financial Services Entertainment & Media Consumer Tech Travel & Leisure Enterprise Tech Telecom Retail
- 17. #MongoDBWebinar | @mongodb Relational Expressive Query Language & Secondary Indexes Strong Consistency Enterprise Management & Integrations
- 18. #MongoDBWebinar | @mongodb The World Has Changed Data Risk Time Cost
- 19. #MongoDBWebinar | @mongodb NoSQL Scalability & Performance Always On, Global Deployments FlexibilityExpressive Query Language & Secondary Indexes Strong Consistency Enterprise Management & Integrations
- 20. #MongoDBWebinar | @mongodb Nexus Architecture Scalability & Performance Always On, Global Deployments FlexibilityExpressive Query Language & Secondary Indexes Strong Consistency Enterprise Management & Integrations
- 21. #MongoDBWebinar | @mongodb Integrating MongoDB and Kafka
- 22. #MongoDBWebinar | @mongodb Where MongoDB Fits Prod 3 2 4 123... Topic A Prod 9 6 7 123... Topic B Filter Filter Merge 5 3 4 123... Topic C Analyze 4 9 6 123... Topic D Take Action Take Action
- 23. #MongoDBWebinar | @mongodb Where MongoDB Fits Prod 3 2 4 123... Topic A Prod 9 6 7 123... Topic B Filter Filter Merge 5 3 4 123... Topic C Analyze 4 9 6 123... Topic D Take Action Store Results Operational Database
- 24. #MongoDBWebinar | @mongodb Where MongoDB Fits Prod 3 2 4 123... Topic A Prod 9 6 7 123... Topic B Filter Filter Merge 5 3 4 123... Topic C Analyze 4 9 6 123... Topic D Take Action Store Results Key Events Operational Database
- 25. #MongoDBWebinar | @mongodb Where MongoDB Fits Prod 3 2 4 123... Topic A Prod 9 6 7 123... Topic B Filter Filter Merge 5 3 4 123... Topic C Analyze 4 9 6 123... Topic D Take Action Store Results Key Events Operational Database
- 26. #MongoDBWebinar | @mongodb Where MongoDB Fits Prod 3 2 4 123... Topic A Prod 9 6 7 123... Topic B Filter Filter Merge 5 3 4 123... Topic C Analyze 4 9 6 123... Topic D Take Action Store Results Key Events Operational Database Reference Data
- 27. #MongoDBWebinar | @mongodb Where K-Streams Fits Prod 3 2 4 123... Topic A Prod 9 6 7 123... Topic B 5 3 4 123... Topic C Analyze 4 9 6 123... Topic D Take Action Store Results Key Events Operational Database Reference Data Kafka Streams
- 28. #MongoDBWebinar | @mongodb MongoDB As a Kafka Producer
- 29. #MongoDBWebinar | @mongodb MessageQueue Customer Data Mgmt Mobile App IoT App Live Dashboards Raw Data Processed Events Distributed Processing Frameworks Millisecond latency. Expressive querying & flexible indexing against subsets of data. Updates-in place. In-database aggregations & transformations Multi-minute latency with scans across TB/PB of data. No indexes. Data stored in 128MB blocks. Write-once-read-many & append-only storage model Sensors User Data Clickstreams Logs Churn Analysis Enriched Customer Profiles Risk Modeling Predictive Analytics Real-Time Access Batch Processing, Batch Views Design Pattern: Operationalized Data Lake Kafka Streams
- 30. #MongoDBWebinar | @mongodb MessageQueue Customer Data Mgmt Mobile App IoT App Live Dashboards Raw Data Processed Events Millisecond latency. Expressive querying & flexible indexing against subsets of data. Updates-in place. In-database aggregations & transformations Multi-minute latency with scans across TB/PB of data. No indexes. Data stored in 128MB blocks. Write-once-read-many & append-only storage model Sensors User Data Clickstreams Logs Churn Analysis Enriched Customer Profiles Risk Modeling Predictive Analytics Real-Time Access Batch Processing, Batch Views Design Pattern: Operationalized Data Lake Configure where to land incoming data Distributed Processing Frameworks Kafka Streams
- 31. #MongoDBWebinar | @mongodb MessageQueue Customer Data Mgmt Mobile App IoT App Live Dashboards Raw Data Processed Events Millisecond latency. Expressive querying & flexible indexing against subsets of data. Updates-in place. In-database aggregations & transformations Multi-minute latency with scans across TB/PB of data. No indexes. Data stored in 128MB blocks. Write-once-read-many & append-only storage model Sensors User Data Clickstreams Logs Churn Analysis Enriched Customer Profiles Risk Modeling Predictive Analytics Real-Time Access Batch Processing, Batch Views Design Pattern: Operationalized Data Lake Raw data processed to generate analytics models Distributed Processing Frameworks Kafka Streams
- 32. #MongoDBWebinar | @mongodb MessageQueue Customer Data Mgmt Mobile App IoT App Live Dashboards Raw Data Processed Events Millisecond latency. Expressive querying & flexible indexing against subsets of data. Updates-in place. In-database aggregations & transformations Multi-minute latency with scans across TB/PB of data. No indexes. Data stored in 128MB blocks. Write-once-read-many & append-only storage model Sensors User Data Clickstreams Logs Churn Analysis Enriched Customer Profiles Risk Modeling Predictive Analytics Real-Time Access Batch Processing, Batch Views Design Pattern: Operationalized Data Lake MongoDB exposes analytics models to operational apps. Handles real time updates Distributed Processing Frameworks Kafka Streams
- 33. #MongoDBWebinar | @mongodb MessageQueue Customer Data Mgmt Mobile App IoT App Live Dashboards Raw Data Processed Events Millisecond latency. Expressive querying & flexible indexing against subsets of data. Updates-in place. In-database aggregations & transformations Multi-minute latency with scans across TB/PB of data. No indexes. Data stored in 128MB blocks. Write-once-read-many & append-only storage model Sensors User Data Clickstreams Logs Churn Analysis Enriched Customer Profiles Risk Modeling Predictive Analytics Real-Time Access Batch Processing, Batch Views Design Pattern: Operationalized Data Lake Compute new models against MongoDB & HDFS Distributed Processing Frameworks Kafka Streams
- 37. #MongoDBWebinar | @mongodb Kafka – What’s Next
- 38. #MongoDBWebinar | @mongodb Kafka Connectors • Confluent-supported connectors (included in CP) • Community-written connectors (just a sampling) JDBC
- 39. #MongoDBWebinar | @mongodb Kafka Futures • Apache Core • Admin API (KIP-4) • Exactly-once delivery semantics • Time-based topic indexing • Kafka Streams • Exactly-once processing semantics • Interactive Queries: enable real-time sharing of application state with other applications • Confluent Platform Enterprise • Multi-cluster views and alerting added to Control Center
- 40. #MongoDBWebinar | @mongodb Next Steps
- 41. #MongoDBWebinar | @mongodb MongoDB Atlas Database as a service for MongoDB MongoDBAtlas is… • Automated: The easiestway to build,launch,and scale apps on MongoDB • Flexible: The only database as a service with all you need for modern applications • Secured: Multiple levels ofsecurity available to give you peace of mind • Scalable: Deliver massive scalability with zero downtime as you grow • Highly available: Your deployments are fault-tolerantand self-healing by default • High performance: The performance you need for your most demanding workloads
- 42. #MongoDBWebinar | @mongodb MongoDB Atlas Features • Spin up a cluster in seconds • Replicated & always- on deployments • Fully elastic: scale out or up in a few clicks with zero downtime • Automatic patches & simplified upgrades for the newest MongoDB features • Authenticated & encrypted • Continuous backup with point-in-time recovery • Fine-grained monitoring & custom alerts Safe & SecureRun for You • On-demand pricing model;billed by the hour • Multi-cloud support (AWS available with others coming soon) • Part of a suite of products & services designed for all phases of your app; migrate easily to different environments (private cloud, on- prem, etc) when needed No Lock-In Database as a service for MongoDB
- 43. #MongoDBWebinar | @mongodb MongoDB Enterprise Advanced • MongoDB Ops Manager or MongoDB Cloud Manager Premium • MongoDB Compass • MongoDB Connector for BI • Encrypted Storage Engine • LDAP / Kerberos Integration • DDL & DML Auditing • FIPS 140-2 Support SecurityTooling • 24 x 7 Support • 1 hr SLA • Emergency Patches • Customer Success Program • On-Demand Training Support License • Commercial License
- 44. #MongoDBWebinar | @mongodb Resources • Data Streaming with Apache Kafka & MongoDB • https://www.mongodb.com/collateral/data-streaming-with-apache- kafka-and-mongodb • Implementing a Kafka Consumer for MongoDB • https://www.mongodb.com/blog/post/mongodb-and-data-streaming- implementing-a-mongodb-kafka-consumer • Tailing the Oplog on a sharded MongoDB Cluster • https://www.mongodb.com/blog/post/tailing-mongodb-oplog-sharded- clusters
- 45. #MongoDBWebinar | @mongodb Old Billingsgate, London 15th November mongodb.com/europe Use my discount code for 20% off: andrewmorgan20
- 46. #MongoDBWebinar | @mongodb Document Data Model Relational MongoDB { customer_id : 1, first_name : "Mark", last_name : "Smith", city : "San Francisco", phones: [ { number : “1-212-777-1212”, dnc : true, type : “home” }, number : “1-212-777-1213”, type : “cell” }] } Customer ID First Name Last Name City 0 John Doe New York 1 Mark Smith San Francisco 2 Jay Black Newark 3 Meagan White London 4 Edward Daniels Boston Phone Number Type DNC Customer ID 1-212-555-1212 home T 0 1-212-555-1213 home T 0 1-212-555-1214 cell F 0 1-212-777-1212 home T 1 1-212-777-1213 cell (null) 1 1-212-888-1212 home F 2
- 47. #MongoDBWebinar | @mongodb Document Model Benefits { customer_id : 1, first_name : "Mark", last_name : "Smith", city : "San Francisco", phones: [ { number : “1-212-777-1212”, dnc : true, type : “home” }, number : “1-212-777-1213”, type : “cell” }] } Agility and flexibility Data model supports business change Rapidly iterate to meet new requirements Intuitive, natural data representation Eliminates ORM layer Developers are more productive Reduces the need for joins, disk seeks Programming is more simple Performance delivered at scale
- 48. #MongoDBWebinar | @mongodb Rich Functionality MongoDB Expressive Queries • Find anyone with phone # “1-212…” • Check if the person with number “555…” is on the “do not call” list Geospatial • Find the best offer for the customer at geo coordinates of 42nd St. and 6th Ave Text Search • Find all tweets that mention the firm within the last 2 days Aggregation • Count and sort number of customers by city Native Binary JSON support • Add an additional phone number to Mark Smith’s without rewriting the document • Select just the mobile phone number in the list • Sort on the modified date { customer_id : 1, first_name : "Mark", last_name : "Smith", city : "San Francisco", phones: [ { number : “1-212-777-1212”, dnc : true, type : “home” }, { number : “1-212-777-1213”, type : “cell” }] } Left outer join ($lookup) • Query for all San Francisco residences,lookup their transactions, and sum the amount by person
- 49. #MongoDBWebinar | @mongodb MongoDB Technical Capabilities Application Driver Mongos Primary Secondary Secondary Shard 1 Primary Secondary Secondary Shard 2 … Primary Secondary Secondary Shard N db.customer.insert({…}) db.customer.find({ name: ”John Smith”}) 1.Dynamic Document Schema { name: “John Smith”, date: “2013-08-01”, address: “10 3rd St.”, phone: { home: 1234567890, mobile: 1234568138 } } 2. Native language drivers 4. High performance - Data locality - Indexes - RAM 3. High availability - Replica sets 5. Horizontal scalability - Sharding … …
- 50. #MongoDBWebinar | @mongodb MongoDB Use Cases Single View Internet of Things Mobile Real-Time Analytics Catalog Personalization Content Management