Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Introducing workload analysis


Published on

SkySQL is the first and only database-as-a-service (DBaaS) to perform workload analysis with advanced deep learning models, identifying and classifying discrete workload patterns so DBAs can better understand database workloads, identify anomalies and predict changes.

In this session, we’ll explain the concepts behind workload analysis and show how it can be used in the real world (and with sample real-world data) to improve database performance and efficiency by identifying key metrics and changes to cyclical patterns.

Published in: Data & Analytics
  • Be the first to comment

Introducing workload analysis

  1. 1. Introducing workload analysis Shane K Johnson Senior Director of Product Marketing MariaDB Corporation 1
  2. 2. Workload analysis – why 2 ● Gain deeper insights into database usage ● Optimize resource allocation ○ Reduce costs, improve performance ○ Rinse and repeat (e.g., day vs. night, weekday vs. weekend) ● Take proactive measures (vs. reactive) ● Maintain quality of service (QoS) ● Build a foundation for autonomous services
  3. 3. Workload analysis – definition ● Categories ○ Transactional vs. analytical ○ Read vs. write vs. mixed ○ Too simplistic ● Discrete queries ○ Which ones to optimize for? ○ Which ones will be hurt? ○ Too many different queries Conventional 3 ● Resource based ● Database state ● Identifiable ● Time bound ○ Cycles and patterns ○ Evolution ● Statistical ○ Distributions ○ Properties Modern
  4. 4. Workload analysis – insight ● Is the workload changing? ● Are workload changes getting smaller or bigger? ● Do workload changes justify further resource optimization? ● How are workload changes impacting the business? 4
  5. 5. Workload analysis – application ● Most important metrics ● Define the workload ● Strong correlation ● Change with/to workload ● Learned by WLA Critical metrics 5 ● Time intervals ● Temporal changes ● Trends and spikes Historical context
  6. 6. Workload analysis – coming next ● Dynamic vs. static ● Per workload vs. global ● Based on change ○ Similarity index ○ Rate, distribution, spread ● No more ○ Manual analysis ○ Needle in a haystack ● Personalized health checks Proactive monitoring ● Maintaining consistency ● Learned QoS metrics ● Predictive alerts ○ Or, autonomous changes Quality of Service (QoS) 6
  7. 7. SkySQL workload analysis application 7
  8. 8. Workload analysis 8 ● SkySQL app that lets users explore database workloads that were automatically detected by our Machine Learning platform. ● It gives easy access to interactive visualizations to help users understand how database workloads change over time.
  9. 9. Machine learning pipeline 9 1. Collect database metrics at 5-sec intervals 2. Extract data from Monitor repository, on an hourly basis 3. Preprocess data to reduce ”noise” and strongly correlated metrics 4. Apply Deep Learning to create working tensor a. 2000+ sample data points, 600+ model steps b. Approximately 100+ critical features 5. Cluster the matrix into workloads that exhibit similar behavior 6. Visualize via D3
  10. 10. Daily max over time 10 Visualize changes in the daily maximum values of 100+ metrics, making it easy to identify historical trends and recurring patterns
  11. 11. Correlated metrics 11 Visualize the collective impact of correlated metrics, identified by deep-learning, on all database workloads (i.e., metrics that change together).
  12. 12. Distribution impact 12 Visualize the spread and distribution of metrics so DBAs can anticipate and optimize resources usage like memory for performance
  13. 13. Metric relationships 13 Ability to pair any of the 100+ critical features to see how they change relative to each other.
  14. 14. DEMO 14
  15. 15. Thank you! Questions? 15