Performance Tuning Guide - EMC Community Network

Planning Capacity and Sizing
Chapter 2
This chapter provides the following capacity and sizing related sections:
• Planning for peak or average loads, page 15
• Planning for workflow database tables, page 17
• Characterizing content, page 17
• Capacity planning worksheet, page 18
• Sizing the system, page 22
The relationship between content (number of documents and average content size), flow (content
ingested per time period), retention policies (content disposition rates), and growth projections affect
your capacity planning. Plan your capacity to accommodate future content and flow. A small system
comprised of a repository with 10 million objects and 2 million activities per day can grow to a large
repository with 10 billion objects and 20 million activities per day.
Plan your capacity to account for content disposition rates and auditability. Solutions that grow to
1 million documents by ingesting 100,000 documents per year for ten years has a different impact
from solutions that ingest and dispose of 1 million documents per day. Solutions that retain an audit
trail for a long time have a significant impact, even if the daily workflow volume is small. After ten
years, a solution which averages only 50 workflows per day can generate an audit trail database
table with 100 million rows.
Planning for peak or average loads
Systems with high transaction volume or large usage (100,000 activities per hour, for example) can
exhibit variable performance during certain time periods. Sizing the system for average rather than
peak loads can result in degraded performance during peak load periods. For mission critical
applications where downtime or performance degradation is unacceptable, size the system for peak
loads.
Gather all use case scenarios during the requirements phase. If there are periods when workloads
spike (Figure 3, page 16), make sure to identify and use these volume metrics when sizing your
system. When load testing, include single user, average-load, and peak-load scenarios.
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