Extract Process Optimization
2017 till now | Oracle, SolarWinds, Dedicated Team
Optimization of the client’s general ledger extract process, which works with large datasets from different sources.
The US national leader in customized insurance, claims and patient safety & risk solutions for physicians, surgeons, dentists and other healthcare professionals, as well as hospitals, senior care and other healthcare facilities.
It was noticed that one SQL-query was performing poorly and required optimization. The task was to find out the reasons which had a negative impact on the system performance and eliminate them.
The problem was revealed during system performance monitoring. While building the query execution plan, the optimizer was more likely to choose a full table scan instead of more efficient use of existing indexes. We also noticed a whole class of the queries having the same behavior.
At the next step, we conducted a root cause analysis of the issue, which showed that the system statistics weren’t valid and they failed to represent the real performance of the system.
The elaborated solution to this system issue contained the following:
- Identifying the time intervals for gathering statistics;
- Gathering and validating system statistics values for the database;
- Finding the balance, since the Database serves both batch (DSS) and online transaction processing (OLTP);
- Gathering daytime, night-time and 24-hour system statistics for the databases, when the workload is expected to be typical of the system;
- Analyzing the gathered values;
- Publishing new system statistics.
As the result, the number of total direct path read events decreased twice. Therefore, some of the bottleneck processes speeded up manifold – from 3 hours run time in average to 10-15 minutes.
Oracle Oracle SQL Trace
Featured case studies:
The customer asked Azati to audit the existing solution in terms of general performance to create a roadmap of future improvements. Our team also increased application performance and delivered several new features.
At Azati Labs, our engineers developed an AI-powered prototype of a tool that can spot a stock market trend. Online trading applications may use this information to calculate the actual stock market price change.
Azati designed and developed a semantic search engine powered by machine learning. It extracts the actual meaning from the search query and looks for the most relevant results across huge scientific datasets.
Azati helped a well-known software integrator to eliminate legacy code, rebuild a complex web application, and fix the majority of mission-critical bugs.
Azati helped a European startup to create a custom logistics platform. It helps shippers to track goods in a real-time, as well as guarantees that the buyer will receive the product in a perfect condition.