ScaleArc

Deep Query Analytics

Gain real-time, actionable insights. Speed troubleshooting.

The first step to solving database-related issues is knowing what is happening on database servers. In most cases, enabling logging or tracing in the database significantly slows performance, making it impractical for production environments.

ScaleArc collects extensive data regarding connections, user logins, SQL queries being executed, and much more - all of them in ScaleArc vs. the database and without sampling of data. ScaleArc's real-time analytics, centralized logging, and historical stats provide the tools you need to pinpoint issues and gain instant insights.

You can use these valuable analytics tools to

  • Track connection spikes that result in poor performance or outages
  • Track a SQL session or SQL connection
  • Pinpoint database performance issues, up to the minute

Tracking connection spikes that result in poor performance or outage
ScaleArc provides you the ability to go into historical connection data and look at connection trends over time. You pick the time window - spanning a few hours or even days - and narrow it down to the exact second when the issue occurred.

Tracking a SQL session or SQL connection
Many app issues can be resolved by getting a trace of all the SQL calls executed in a SQL connection. ScaleArc's analytics show all of the SQL calls made by the application in a human-readable, four-quadrant graph that highlights problem queries. You can click into the pattern to see all of the individual SQL calls made by the application displayed.

From within the analytics pane, you can take the SQL session ID and execute a search within the SQL query logs to track a SQL session.

Pinpointing database performance issues
ScaleArc aggregates and presents non-sampled data points, rapidly extracts key query patterns, and visually displays the findings – instantly highlighting slow and frequent queries. You can focus on the query patterns in the top-right corner, which includes the slow-but-frequent queries. This approach is much more accurate when compared to analyzing slow query logs because it accounts for every query's impact on the database server.

You can also use ScaleArc analytics to compare patterns from different hours. This process can help identify any new queries that might be causing a change in database performance. One of our customers was able to track down a SQL injection attack that was issuing a sleep within the "where" clause. Using the "compare patterns" feature in our analytics, our customer readily identified the new query and immediately blocked it using our query firewall.

Sazze Case Study

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