Geode Performance Architecture for the Agile Enterprise Using Cloud Native API's
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The document discusses the performance architecture of geode within agile enterprises, focusing on cloud-native APIs. It outlines incremental testing approaches, governance for using geode, and continuous production monitoring strategies. Additionally, it emphasizes cost optimization and scalability benefits through the pivotal cloud cache and centralized tools.
Geode Performance Architecture for the Agile Enterprise Using Cloud Native API's
- 1. Geode Performance Architecture for the Agile Enterprise Using Cloud Native API's Pivotal SpringOne Platform Conference 2018 Joshua N. Haggerty and Amit Madan September 2018
- 2. 22 Incremental Testing Approach Overview Performance Architecture Approach in Agile Environment Design Governance Questions Continuous Production Monitoring Pivotal Cloud Cache changes the Approach
- 3. 3 Performance Architecture Approach in Agile Environment Agile’s Strengths Can Be Challenges • Continuous Integration • Requirement changes parallel to code development • Inadequate API testing • Time to Market • Inadequate end to end testing • Parallel Development Teams • Code bases and transaction volumes merge in to common application environment Design Governance Incremental Testing Continued Monitoring Micro Service A Micro Service B Micro Service C Micro Service D Micro Service E Micro Service F Micro Service G Micro Service H Micro Service I Micro Service … Z Common Geode Cluster Approach MobileCardsBanking
- 4. 4 Design Governance When to use Geode How is Geode Being Used How to use Geode Good Governance 1. When to Use Geode Approved Use Cases for Geode from a product and enterprise perspective 2. How to Use Geode Follow Published Best practices Ensure Geode Region Settings Partitioned vs. Replicated Expiration time Payload Size Transaction load Establish Geode Software Contracts 3. How is Geode Being Used Monitor and audit software contracts
- 5. 5 Incremental Testing Approach Product Testing Automated Testing at the GEODE product version level API Level Testing Automated Testing at a specific API level End to End Testing Automated Testing at release level from end to end
- 6. 6 Geode Incremental Testing Cache Harness Geode Cluster Geode Product Testing • Identify ideal architecture for environment • Optimize configuration for client connection pool and geode cluster • Identify capacity limitations per environment • Execute needed failover scenarios for expected behavior Service A Geode Cluster API Testing • Load test specific API/service • Ensure standard metrics for service such as memory usage, cpu usage, connections, threads, etc… • Identify any region specific configurations such as indexes, timeouts, replication schemes Front end Application PCF Services End to End Testing • Test at enterprise level or smaller logical component • Validate expected volume requirements at API level • Ensure payloads and expected memory footprints • Compare deltas at release level • Monitor standard metrics Geode Cluster
- 7. 7 Continuous Production Monitoring • Identify a team and a process to reconcile production metrics against expected workloads • Track metrics at a region level to audit software contracts • Transactions per second • Read vs write transaction breakdown • Average payload sizes • Number of live entries • Function calls • Ensure Capacity at Cluster level • Monitor overall memory usage • CPU • Average Connections • Connection Spikes • Network • Validate Performance Test Scenarios
- 8. 8 Pivotal Cloud Cache Evolves The Approach Org Space Micro Serivce Micro Serivce Micro Serivce Micro Service Space Micro Serivce Micro Serivce Micro Serivce Micro Service • Decreases cost of ownership through platform optimization • Geode cluster at space level (org level) • Services have less common dependency • Easier provisioning for transient environments • Increased visibility for developers • Centralizes toolset and skillset within PCF • Eases horizontal scalability • Can increase hardware footprint PCC PCC
- 9. 9 Questions