The Performance Monitoring homepage is the main view where you can search or browse for transaction data. Filter by search conditions or choose from a list of sortable transactions in the table. As soon as a transaction of interest has been rooted out, you can investigate further by clicking it for its summary view.
The search bar operates similarly to the one in the Discover Query Builder. For example, you can refine your transaction search to a specific release by using
release: as a key field and assigning the version as a value.
Compare graphs side by side to find any relevant correlations. For example, if an Apdex score dips significantly during a given time period, you'll be able to visualize whether throughput spiked in the same time interval. The global header and search conditions will also enable you to filter these graphs even more. The display options are listed below:
Below the graphs are a list of transactions that may be filtered down based on global header and search condition filters. The provided columns offer details about each transaction.
If you have transactions you frequently return to, you can mark up to 10 transactions as a key transaction by clicking the star in the corresponding row. Clicking the star ensures that these key transactions are always surfaced to the top of the list. You can also mark a transaction as a key transaction in its corresponding summary view.
Trends surfaces transactions that have had significant changes in their performance over time. You can find the Trends View by toggling the tab in the Performance homepage's upper right corner.
Trending transactions are calculated first by filtering out transactions with large throughput fluctuations, determining the baselines of the two halves of the duration, and calculating their percentage change. Regressions are any percentage changes greater than zero, while improvements are percentage changes less than zero.
Confidence option allows you to filter trends based on High or Low statistical significance. By default, only high confidence, statistically significant results are shown. Sentry uses a t-test to compare the before and after periods of the transaction and assign a confidence score.
For example, looking at a transaction over two weeks with the default P50 baseline, if the first week had a
p(50)=100s, and the second a
p(50)=110s, the percentage is
(110/100)-1 = 0.1. The example transaction had a performance regression of
The more data is available, the more the accuracy of the trend improves. For this reason, trends perform best over long periods and with transactions with large counts.
While we default the baseline calculation to use P50, you can change the baseline calculation via the dropdown. Other options include P75, P95, P99, and average, and this baseline is used to determine the percentage change.