Geode: Much More Than a Cache
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This document discusses several use cases for Geode, an open source distributed data platform: 1. Global data caching for a hotel marketing system, with property data cached globally for improved performance. 2. Enabling microservices by allowing each service to have its own data store for agile development and faster time to market. 3. Transaction processing for a rail ticketing system, using Geode to update seat availability and prevent double bookings. 4. Event processing for credit card fraud detection, with an initial fraud screening in real-time as customers and merchants wait. 5. Serving as a data-aware compute grid for investment performance reporting, by storing and accessing large amounts of portfolio
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Geode: Much More Than a Cache
- 1. Geode, Much More Than a Cache Randy May @maywr
- 2. Speaker Information 2 Name: Randy May Role: Data Engineer at Pivotal Contact Information: rmay@pivotal.io @maywr on Twitter
- 3. Geode Use Cases 3 ● Global Data Cache: Hotel Marketing ● Microservice Enabler : Any Software Driven Enterprise ● Transaction Processing: Rail Ticketing ● Event Processing: Credit Card Fraud Detection ● Data Aware Compute Grid: Investment Performance Reporting ● Streaming Data Capture: Vehicle Fleet Operations
- 4. Geode for Global Data Caching: Background 4 • Customer systems rely heavily on “property data” • The information does not change very often and is heavily queried. • The systems that use the data are globally dispersed either at the actual properties or an in-region data center. • The system of record for the property data is a DB running on a mainframe in the HQ data center. • All regions need all property data.
- 5. Geode for Global Data Caching: Blueprint 5
- 6. Geode as a Micro Service Enabler: Background 6 Situation: any modern, software-centric enterprise ... Company recognizes the need to modernize in order to • deliver new products and experiences faster • generally to be more agile However .... There is a huge existing infrastructure that • must continue to function • can’t be modernized all at once
- 7. Micro Services Require Their Own Data Store 7 To Deliver Value Faster, You Need All of These ... ● Agile Development Practices ● Devops Approach and Platform (e.g. Cloud Foundry) ● Agile Architecture (Micro Services) and .. ● An Agile Data Store See https://martinfowler.com/articles/microservices.html#DecentralizedDataManagement
- 8. Agile Requirements • Each service controls its own data store • Time to market more important than strict governance • NoSQL API Why Micro Services Require Their Own Data Store Legacy Requirements • Many apps share the DB • Strict Governance of Schema • SQL API
- 9. Geode as a Micro Service Enabler: Blueprint 9
- 10. Geode for Transaction Processing: Background 10 ● Rail ticketing system ● Very high volume, high concurrency ● The available seats on a trip can be highly contended ● To avoid selling the same seat many times, transactions must be used to update "seats-available"
- 11. Geode for Transaction Processing: Blueprint 11
- 12. Geode for Event Processing: Background 12 ● Credit Card Fraud Detection ● Initial Fraud Screen Happens While Customer and Merchant Wait
- 13. Credit Card Fraud: Process Overview 13 Data science model is scored in real time to make fraud determination.
- 14. Geode for Event Processing: Blueprint 14
- 15. Geode as a Data Aware Compute Grid: Overview 15 ● Customer already does monthly portfolio performance statements but now wants to add online capabilities. ● This is a fairly heavy computation that was traditionally handled as a long running batch job. ● Computing rate of return requires you to know the price of each holding in the portfolio on every day. This is potentially a lot of data!
- 16. The Portfolio Performance Reporting Process Date Symbol Holdings 01/01/2018 ACME 100 06/30/2018 ACME 150 Date Symbol Holdings Value 01/01/2018 ACME 100 13,200 01/02/2018 ACME 100 13,420 01/03/2018 ACME 100 13,370 ... ... ... .... This format preferred for storage This format required for computation Holdings History Daily Values
- 17. The Portfolio Performance Reporting Process
- 18. Geode as a Data Aware Compute Grid: Blueprint 18
- 19. Geode for Steaming Data: Background 19 ● Customer operates a fleet of vehicles that constantly produce data like location and engine diagnostics. ● The challenge is to turn the stream of data into useful business insights like "where is this vehicle now" ?
- 20. Geode for Streaming Data: Blueprint 20
- 21. Geode: Much More than a Cache ! 21 ● Global Data Cache: Hotel Marketing ● Microservice Enabler : Any Software Driven Enterprise ● Transaction Processing: Rail Ticketing ● Event Processing: Credit Card Fraud Detection ● Data Aware Compute Grid: Investment Performance Reporting ● Streaming Data Capture: Vehicle Fleet Operations
- 22. Geode: Much More than a Cache ! 22
- 23. Thank You !
Editor's Notes
- This is a caching scenario but it’s a globally distributed cache. Remarks Data is as current as possible given limitations imposed by the network Note that the push approach means that the customer will never wait while property data is retrieved We also have the traditional benefit of caching. A dramatic reduction in the read traffic to the DB, which in turn saves $
- We inevitably need a second data store Of course that creates another problem (data synchronization)
- Geode is certainly not acting as a cache in this situation but as a fully independent data store. Remarks About Consistency This solution obviously involves some eventual consistency model which brings its own challenges BUT often the actual business model already assumes eventual consistency the alternative, complete synchronization, can be catastrophic for performance great paper about this tradeoff by "Helland" called "Building on Quicksand" Other Ways to Do This There are many ways to do this - for example using Geode write-behind Directing reads to Geode but writes to the original SOR At any rate, this is certainly not a cache. This is Geode as a full data store complete with queries and transactions.
- Ability to spread data by a chosen attribute, such as rail line, allows solution scale by exploiting opportunities to divide and conquer. Server side Functions mean booking transactions can last microseconds, reducing contention. Historical Note: This sort of use cases drove the early development of GemFire. Scenarios like this are why Geode prioritizes consistency over other qualities. For example it always does synchronous replication so that updates will not be lost if there is a failure.
- The inputs to the model, called “features” will change as a result of the a card swipe. Examples of features might include “location of last card swipe”, “spend in last hour/day/week”, “current account balance”, “merchant period to date charges”.
- Partition by CC # GemFire Function contains the code. Regions contain the data Remarks Again, we see partitioned regions playing a vital role in parallelizing the problem and enabling scale. Note that the data needed for generating the features and evaluating the model are all in the same process. Alternative Approach - If the feature can be updates asynchronously then AEQ can be used Similar Systems - Event Processing == High Speed Decision Making A similar pattern is also used in online ad. serving where the "event" is actually serving a web page and processing the event means selecting the best ad to embed in the page.
- This is an in-memory , data-aware, map-reduce ! Partition by Symbol - the holding are sliced by symbol and "scattered" to the nodes where the price data resides Each node computes the daily values for its portion of the symbols The daily values are gathered into the Rate of Return Service where the final calculation occurs (that’s the reduce part) Remarks This showed a more advanced problem where there was no way to completely parallelize either by symbol or by customer. In contrast with a pure compute grid approach or even an hdfs based approach, we are not touching disk to read in prices - they just sit there in memory we are also not moving prices over the network So that is how Geode can be used to do data-aware map reduce tasks
- Data is Partitioned by Vehicle The actual telemetry data is not kept in the grid for very long AEQs are used to process the stream and compute useful summaries and projections Those in turn drive dashboards. Remarks yet again, intelligent partitioning allows the work to be parallelized The use of the AEQ in a very high volume streaming ingest system is essential because you don't want anything to slow down the basic "put" operations This system illustrates a common principle. Use Geode to hold the "right now" view of the business and use a different technology for analytic and BI tasks that require history. There we have Geode for streaming data ingest
- I hope this has been informative and that you've learned something new about how Geode is used. Mostly I hope you now see that Geode is much more than a cache. Thank You!