Provides interactive dashboard for Skia performance data.
The code for the server along with VM instance setup scripts is kept in:
This is the general flow of data for the Skia performance application. The frontend is available at http://skiaperf.com.
+-------------+ | | | Browser | | | | | | | +----------^--+ | +--------------------+----+-----+ | GCE Instance|skia-testing-b | | | | | +-----------+----------+ | | | Nginx | | | | | | | +--------^-------------+ | | | | | +----------+-------------+ | | | Perf (Go) | | | | ^ ^ | | | +------------------------+ | | | | | | | | | | | | +------------------+ | | | | |Tile Pipeline (Go)| | | | | | ^ | | | | | +--+---------------+ | | | | | | | +-------------------------------+ | | | | +---------+-+ | | +-------+--+ | MySQL | | | | Google | | | | | | Storage | | | | | | | | | | | | | | | | | | | | | | | | | +-----------+ | | +----------+ | | +-+----v---+ | Tile | | Repo | | | | | | | | | +----------+
Perf is a Go application that serves the HTML, CSS, JS and the JSON representations that the JS needs. It loads test results in the form of ‘tiles’ from the Tile Repo. It combines that data with data about commits and annotations from the MySQL data base and serves that the UI.
The Tile Pipeline is a separate application that periodically queries for fresh data from Google Storage and then writes Tiles into the Tile Repo.
Tile Repo will be represented internally as an interface, the first implemetation will be as files on the local disk, with a directory tree that contains Go gob files called tiles.
Each tile contains exactly 128 points of every trace for a dataset. The one exception being the last tile, which may contain less that 128 points; see below for an explanation of that. The Tile Repo directory structure is:
When navigating the UI users can select the tiles they are looking at (<, >) and also change the scaling factor that they are looking at (+,-).
The URL structure for retrieving Datasets is TBD.
For each point if the user wants to zoom out, add 1 to the scale factor and divide tilenumber by two. Do the opposite to zoom in. To move forwards or backwards in time add or subtract 1 to the tile number. The actual UI mechanisms for navigating around traces are TBD, this is just a description of how the tiles are arranged.
TBD based on new ingestion code.
We use the https://github.com/golang/glog for logging, which puts Google style Error, Warning and Info logs in /tmp/glog on the server under the ‘perf’ account.
Starting the application is done via /etc/init.d/skiaperf which does the backgrounding itself via start-stop-daemon, which means that if the app crashes when first starting then nothing will make it to the logs. To debug the cause in that case edit /etc/init.d/skiaperf and remove the --background flag and then run:
$ sudo /etc/init.d/skiaperf start
And you should get stdout and stderr output.
Users must be logged in to access some content or to make some changes in the application, such as changing the status of perf alerts. User authentication is handled through OAuth 2.0, in this case specifically tied to the Google implementation. Once the OAuth 2.0 permission grant is complete then the users email is used as an identifer. The authentication is not stored on the server, instead it is stored as a cookie in the browser and verified when authentication is needed.
In Go the login.LoggedInAs(), see go/login/login.go.
Monitoring of the application is done via Graphite at http://skiamonitor.com. Both system and application level metrics are monitored.
A Cloud SQL (a cloud version of MySQL) database is used to keep information on Skia git revisions and their corresponding annotations. The database will be updated when users add/edit/delete annotations via the dashboard UI.
MySQL Flags to set:
max_allowed_packet = 1073741824
All passwords for MySQL are stored in valentine (search “skiaperf”).
To connect to the database from authorized network (including skia-testing-b GCE):
$ mysql -h 220.127.116.11 -u root -p mysql> use skia mysql> show tables;
Initial setup of the database, the users, and the tables:
Create the database and set up permissions. Execute the following after you connect to a MySQL database (not necessary for SQLite).
CREATE DATABASE skia; USE skia; CREATE USER ‘readonly’@‘%’ IDENTIFIED BY ; GRANT SELECT ON . TO ‘readonly’@‘%’; CREATE USER ‘readwrite’@‘%’ IDENTIFIED BY ; GRANT SELECT, DELETE, UPDATE, INSERT ON . TO ‘readwrite’@‘%’;
Create the versioned database tables.
We use the ‘migratedb’ tool to keep the database in a well defined (versioned) state. The ‘db_conn_string’ flag allows to specify the target database. By default it will try to connect to the production environment. But for testing a local MySQL database can be provided. If it cannot connect to MySQL it will fall back to SQLite.
Bring the production database to the latest schema version:
$ migratedb -logtostderr=true
Bring a local database to the latest schema version:
$ migratedb -logtostderr=true -db_conn_string=“root:%s@tcp(localhost:3306)/skia?parseTime=true”
Bring a local SQLite database to the latest schema version:
$ migratedb -logtostderr=true -db_conn_string=""
Nginx acts as a proxy to the backend app, which is configured to run on port 8000, and serves the app on port 443 (HTTPS). Port 80 just redirects to 443. The config for the nginx server is held in setup/sys/perf_nginx, which is copied into place during installation. Nginx is monitored and kept running by monit.
The clustering is done by using k-means clustering over normalized Traces. The Traces are normalized by filling in missing data points so that there is a data point for every commit, and then scaling the data to have a mean of 0.0 and a standard deviation of 1.0. See the docs for ctrace.NewFullTrace().
The distance metric used is Euclidean distance between the traces.
After clustering is complete we calculate some metrics for each cluster by curve fitting a step function to the centroid. We record the location of the step, the size of the step, and the least squares error of the curve fit. From that data we calculate the “Regression” value, which measures how much like a step function the centroid is, and is calculated by:
Regression = StepSize / LeastSquaresError.
The better the fit the larger the Regression, because LSE gets smaller with a better fit. The higher the Step Size the larger the Regression.
A cluster is considered “Interesting” if the Regression value is large enough. The current cutoff for Interestingness is:
|Regression| > 150
Where negative Regression values mean possible regressions, and positive values mean possible performance improvement.
A dashboard is needed to report clusters that look “Interesting”, i.e. could either be performance regressions, improvements, or other anomalies. The current k-means clustering and calculating the Regression statistic for each cluster does a good job of indicating when something Interesting has happened, but a more structured system is needed that:
The last step, finding clusters that are the same, will be done by fingerprinting, i.e. use the first 20 traces of each cluster will be used as a fingerprint for a cluster. That is, if a new cluster has some (or even one) of the same traces as the first 20 traces in an existing cluster, then they are the same cluster. Note that we use the first 20 because traces are stored sorted on how close they are to the centroid for the cluster.
Algorithm: Run clustering and pick out the “Interesting” clusters. Compare all the Interestin clusters to all the existing relevant clusters, where “relevant” clusters are ones whose Hash/timestamp of the step exists in the current tile. Start with an empty “list”. For each cluster: For each relevant existing cluster: Take the top 20 keys from the existing cluster and count how many appear in the cluster. If there are no matches then this is a new cluster, add it to the “list”. If there are matches, possibly to multiple existing clusters, find the existing cluster with the most matches. Take the better of the two clusters (old/new) based on the better Regression score, i.e. larger |Regression|, and update that in the “list”. Save all the clusters in the “list” back to the db.
This algorithm should keep already triaged clusters in their triaged state while adding new unique clusters as they appear.
Let's say we have three existing clusters with the following trace ids: C, C, C[3,4] And we run clustering and get the followin four new clusters: N, N, N, N In the end we should end up with the following clusters: C or N C C[3,4] or N or N N Where the 'or' chooses the cluster with the higher |Regression| value. Each unique cluster that's found will be stored in the datastore. The schema will be: CREATE TABLE clusters ( id INT NOT NULL AUTO_INCREMENT PRIMARY KEY, ts TIMESTAMP NOT NULL, hash TEXT NOT NULL, regression FLOAT NOT NULL, cluster MEDIUMTEXT NOT NULL, status TEXT NOT NULL, message TEXT NOT NULL ); Where: 'cluster' is the JSON serialized ClusterSummary struct. 'ts' is the timestamp of the step in the step function. 'status' is "New" for a new cluster, "Ignore", or "Bug". 'hash' is the git hash at the step point. 'message' is either a note on why this cluster is ignored, or a bug #. Note that only the id may remain stable over time. If a new cluster is found that matches the fingerprint of an exisiting cluster, but has a higher regression value, than the new cluster values will be written into the 'clusters' table, including the ts, hash, and regression values.
Results from Trybots are loaded via the ingester on a much faster schedule, every minute by default, and are stored in the MySQL database keyed by the Rietveld issue number. Note that this means only tries associated with a Rietveld will be usable by Skiaperf, which is a conscious decision.
The actual try data will be stored as the JSON serialized Go struct that stores all the measured values, types.TryBotResults.
Normal Trace IDs are of the form:
Where the names come from the buildbot description, the test name, and the configuration under which the test was run.
There are two other forms of trace ids:
Calculated traces - A trace that started out with a normal trace id, but then had a calculation performed on it. Calculated trace ids are not stored in shortcuts, but are presumed to be regenerated by any formula traces, which are stored in shortcuts.
Calculated traces have IDs that begin with !. For example:
Formula traces - A formula trace contains a formula to be evaluated which may generate either a single Formula trace that is added to the plot, such as ave(), or it may generate multiple calculated traces that are added to the plot, such as norm(). Note that formula traces are stored in shortcuts and added to plots even if it contains no data.
Formula traces have IDs that begin with @. For example:
The UIs showing line plots of selected traces and the clusterings are good ways to examine and diagnose the performance of a small set of traces across the commit timeline. However, it is not easy to use them for answering questions like: “How does the performance of gpu config compare with 8888 on the SKP benches across various platforms?”, “What's the worst-performing SKPs on x86 vs. x86_64, and are they worst on a specific OS?”, and “How do I pinpoint the set of potential performance changes introduced by a trybot run with my CL?”. In this case we care more about comparing the most recent data values by different configs and platforms, instead of changes along the commit timeline.
To show overall comparison results across more dimensions, we use a table to visualize the data. We use the same query interface for retrieving the set of bench data of user's choice, but ask user to specify the search vertical (arch, config, os, etc.) and select exactly two configs from it to compare against in the query (say, “8888” and “gpu”). We then organize the data to calculate the ratio of the benches from the two choices in the criteria (vertical) where all other parameters are the same. For instance, we calculate the ratio of benches in the “config” vertical from the following two traces:
and put the value into the cell in a table that has row_gradient_create_opaque_640_480 and column x86_64:HD7770:ShuttleA:Win8. Basically, the table row will be the “test” name, and the column will be the rest of the keys. The number of columns will be the number of perf bots we run (20+ for now).
The value will then tell us if the performance is better (<1) or worse (>1) for gpu against 8888. We can then heatmap-color the table cells by their value ranges, to provide a visual way for users to identify the problems in cell groups. By sorting the rows with aggregated performance, users will be able to pinpoint the benches with worst/best relative performance to look into.
The same visulaization can be used for visualizing trybot results as well. When user selects results from a recent trybot run (which is continually polled from Google Storage as the ingester does, organized by try issue numbers and buildbot / build numbers), we pair the most recent bench results from regular buildbot runs with the corresponding trybot bench results with identical trace keys, and show their ratios in the table with heatmap-colored cells. The table row will still be the “test” names, but the columns will concatenate all the other verticals, such as x86_64:HD7770:ShuttleA:Win8:gpu. Users can control which set of trybot results to show together with regular data, thus the number of table columns is dynamic.
We can also add an option for users to specify a CL, so we use the available bench data closest to that CL (either before or after) for visualization.
Another option is to have users provide two CLs and use the UI to show their diffs on common traces.
The server is started and stopped via:
sudo /etc/init.d/skiaperf [start|stop|restart]
But sysv init only handles starting and stopping a program once, so we use Monit to monitor the application and restart it if it crashes. The config is in:
See the README file.