Buildbot/Talos/Data
Talos Data
Raw data is generated by Talos. We apply some filters to summarize and reduce the data, then we post it to a server:
- Graphserver
- Perfherder
Terminology
Job
When a build is completed we run a series of test (unittest and performance "talos") jobs. Each job reserves a machine for itself, then runs the script which sets up, installs, executes the test, generates the results, and cleans up after itself. In general we try to ensure the jobs complete in 30 minutes or less.
For Talos we have a series of jobs and each job runs 1 or more tests (with the exception of tp and xperf, jobs run 2-4 suites at a time). For the purposes of discussing data we will refer to each test as a suite. A suite would be something like 'ts_paint', 'Canvasmark', or 'tp5'. Each Suite will run its respective subtests, and summarize itself properly. When all the suites in a job have completed, the results will be output (uploaded in some cases) and we will be able to look for regressions, view data in a graph, and query for the summarized data.
Suite
A collection of subtests which run. The subtest results are summarized to the suite level. Often these are referred to as 'tests'. Some examples are "tresize", "TART", "tp5", "ts_paint".
- in graph server this is the lowest level of granularity available in the UI
- in Perfherder a suite is referenced as a 'Summary' (e.g "tp5o summary opt")
Subtest
A specific test (usually a webpage to load) which we collect replicates (numbers) from. Typically we run many cycles of each subtest to build up a representative collection of replicates to make sure the data is meaningful.
- in graph server Talos upload a single number for each subtest, the replicates are summarized by Talos prior to uploading.
- in Perfherder the subtest data is preserved as raw replicates as well as summarized by Talos. We use the summarizations when showing a graph.
Replicates
Replicates refer to the single numbers or data points we collect while executing a talos test. In this regard, we collect a series of numbers (usually 20 or more) for each subtest. Each of these 20+ numbers are called replicates.
We do filtering on the replicates, mainly because the first few replicates are not a representative sample of the remaining replicates we collect. The one exception would be internal benchmarks (generally suites which measure something other than time). For Benchmarks, there is usually a special formula applied to the replicates.
Subtest Filters
We have a variety of filters defined for Talos. I will explain what each filter is, and you can see the exact settings used for each filter by looking at the individual tests.
ignore_first
This filter ignores the first 'X' replicates allowing us to ignore warmup runs.
- input: an array of subtest replicates
- returns: an array of replicates
- source: filter.py
- used in most tests with X=1, X=2, and X=5 (5 is the normal case)
median
This filter takes in an array of replicates and returns the median of the replicates (a single value).
- input: an array of subtest replicates
- returns: a single value
- source: filter.py
- used in most tests
mean
This filter takes in an array of replicates and returns the mean value of the replicates (a single value).
- input: an array of subtest replicates
- returns: a single value
- source: filter.py
- used in kraken for subtests
dromaeo
This filter is a specific filter defined by dromaeo and respects the replicates as every 5 replicates represents a different metric being measured.
- input: an array of dromaeo (DOM|CSS) subtest replicates
- returns: a single number (geometric_mean of the metric sumarization)
- source: filter.py
- used in dromaeo_dom and dromaeo_css to build a single value for the subtests
v8_subtest
- input: an array of v8_7 subtest replicates
- returns: a single value representing the benchmark weighted score for the subtest
- source: filter.py
- used in v8_7 for the subtests
NOTE: this deviates from the exact definition of v8 as we retain the Encrypt and Decrypt as subtests (instead of combining them into Crypto) as well as keeping Earley and Boyer (instead of combining them into EarleyBoyer). There is a slight tweak in the final suite score, but it is <1% different.
Suite Summarization Filters
Once we have a single number from each of the subtests, we need to generate a single number for the suite. There are 4 specific calculations used.
geometric_mean
This is a standard geometric mean of the data:
- inputs: array of subtest summarized data points (one point per subtest)
- returns: a single value representing the geometric mean of all the subtests
- source: filter.py
- used for most tests
v8_metric
this is a custom metric which take the geometric_mean of the subtests and multiplies it by 100.
- inputs: array of v8 subtest summaries
- returns: a single v8 score
- source: output.py
- used for v8 version 7 only
Canvasmark_metric
This is the metric used to calculate the Canvasmark score from the subtest summarized results. Essentially it is a sum of the subtests.
- inputs: array of Canvasmark subtest results
- returns: a single Canvasmark score
- source: output.py
- used for Canvasmark only
js_metric
This is the metric used to calculate the Kraken score from the subtest summarized results. Essentially it is a sum of the subtests.
- inputs: array of Kraken subtest results
- returns: a single Kraken score
- source: output.py
- used for Kraken only
Perfherder
Perfherder ingests data from talos by parsing the raw log, then it stores the data in a database while preparing it for regression detection and displaying on graphs.
Raw Data
In the log files, we look for "TALOSDATA: " text followed by a valid json blob. An example TALOSDATA blob looks like:
[{"talos_counters": {}, "results": {"tresize": [23.26174999999999, 22.99621666666672, 22.66563333333331, 23.99620000000002, 22.940849999999948, 22.26951666666664, 22.975350000000006, 24.96453333333337, 23.6878333333334, 23.21740000000001, 24.743699999999976, 23.507333333333282, 22.927800000000033, 22.292066666666653, 23.28364999999999, 23.361950000000004, 22.18191666666666, 22.996466666666684, 23.54029999999997, 22.873883333333342]}, "summary": {"suite": 23.21740000000001, "subtests": {"tresize": {"std": 0.7716690474213389, "min": 22.18191666666666, "max": 24.96453333333337, "median": 23.21740000000001, "filtered": 23.21740000000001, "mean": 23.254913333333334}}}, "test_machine": {"platform": "x86", "osversion": "Ubuntu 12.04", "os": "linux", "name": "talos-linux32-ix-040"}, "testrun": {"date": 1440091515, "suite": "tresize", "options": {"responsiveness": false, "cycles": 20, "tpmozafterpaint": true, "shutdown": false, "rss": false}}, "test_build": {"name": "Firefox", "version": "43.0a1", "id": "20150820095841", "branch": "Mozilla-Inbound-Non-PGO", "revision": "bb85ec539217b9d3a5e83c40538d8565d292e72b"}}, {"talos_counters": {}, "results": {"Plasma - Maths- canvas shapes": [545.0, 572.0, 598.0, 662.0, 588.0], "Asteroids - Shapes- shadows- blending": [748.0, 737.0, 720.0, 742.0, 743.0], "Asteroids - Bitmaps- shapes- text": [1031.0, 1011.0, 913.0, 1063.0, 888.0], "Arena5 - Vectors- shadows- bitmaps- text": [892.0, 738.0, 900.0, 920.0, 806.0], "Asteroids - Vectors": [675.0, 735.0, 659.0, 789.0, 768.0], "3D Rendering - Maths- polygons- image transforms": [306.0, 434.0, 388.0, 426.0, 389.0], "Pixel blur - Math- getImageData- putImageData": [1291.0, 1435.0, 1553.0, 1461.0, 1521.0], "Asteroids - Bitmaps": [435.0, 418.0, 410.0, 403.0, 380.0]}, "summary": {"suite": 6204.0, "subtests": {"Plasma - Maths- canvas shapes": {"std": 34.19064199455752, "min": 572.0, "max": 662.0, "median": 593.0, "filtered": 593.0, "mean": 605.0}, "Asteroids - Shapes- shadows- blending": {"std": 9.233092656309694, "min": 720.0, "max": 743.0, "median": 739.5, "filtered": 739.5, "mean": 735.5}, "Asteroids - Bitmaps- shapes- text": {"std": 71.23333138355947, "min": 888.0, "max": 1063.0, "median": 962.0, "filtered": 962.0, "mean": 968.75}, "Arena5 - Vectors- shadows- bitmaps- text": {"std": 73.40980860893181, "min": 738.0, "max": 920.0, "median": 853.0, "filtered": 853.0, "mean": 841.0}, "Asteroids - Vectors": {"std": 49.37294299512639, "min": 659.0, "max": 789.0, "median": 751.5, "filtered": 751.5, "mean": 737.75}, "3D Rendering - Maths- polygons- image transforms": {"std": 20.94486810653149, "min": 388.0, "max": 434.0, "median": 407.5, "filtered": 407.5, "mean": 409.25}, "Pixel blur - Math- getImageData- putImageData": {"std": 46.82680856090878, "min": 1435.0, "max": 1553.0, "median": 1491.0, "filtered": 1491.0, "mean": 1492.5}, "Asteroids - Bitmaps": {"std": 14.16642156650719, "min": 380.0, "max": 418.0, "median": 406.5, "filtered": 406.5, "mean": 402.75}}}, "test_machine": {"platform": "x86", "osversion": "Ubuntu 12.04", "os": "linux", "name": "talos-linux32-ix-040"}, "testrun": {"date": 1440091515, "suite": "tcanvasmark", "options": {"responsiveness": false, "tpmozafterpaint": false, "tpchrome": true, "tppagecycles": 1, "tpcycles": 5, "tprender": false, "shutdown": false, "cycles": 1, "rss": false}}, "test_build": {"name": "Firefox", "version": "43.0a1", "id": "20150820095841", "branch": "Mozilla-Inbound-Non-PGO", "revision": "bb85ec539217b9d3a5e83c40538d8565d292e72b"}}]
Filtering & Calculations
When the raw data comes in, we look for the summary tag in the json.
{"suite": 23.21740000000001, ... }
In this case we use 23.217 for the value inside of perfherder. This is the value that will be used for calculating alerts, displaying points on the graph, and for data when comparing two revisions.
In all cases there should be a 'subtests' field as well that lists out each page loaded along with a set of values:
"subtests": {"tresize": {"std": 0.7716690474213389, "min": 22.18191666666666, "max": 24.96453333333337, "median": 23.21740000000001, "filtered": 23.21740000000001, "mean": 23.254913333333334}
These values are used in the sub test specific view (not the suite summary). When viewing a graph, you can switch between different values for each data point to see what the mean, median, etc. are. This is where we get the fields. In addition, the default value is the 'filtered' value, this takes into account filters (ignore first 'x' data points, median|mean, etc.) on the raw data so we have summarized data being calculated at a single point.
Each suite has the ability to set custom filters and keeping this logic inside of talos ensures that it is always done in a single place, in source code, where developers can easily look and find it.
Graph Server
Data is packaged as a file in an HTTP post object.
VALUES/AVERAGE
Two different types of data to be sent:
- A single value to be stored as the 'average' in the test_runs table
- A set of (interval, value) pairs to be stored in the test_run_values table, 'average' to be calculated by collector script
First type will be called 'AVERAGE' second called 'VALUES'. All data is formatted using comma separated notation.
date_run = seconds since epoch (linux time stamp) page_name = is unique to pages when combined with the pageset_id from test table
- for sending interval, value pairs
START VALUES machine_name,test_name,branch_name,ref_changeset,ref_build_id,date_run interval0,value0,page_name0 interval1,value1,page_name1 ... intervalEND,valueEND,page_id END
- for sending a single value
START AVERAGE machine_name,test_name,branch_name,ref_changeset,ref_build_id,date_run value0 END
Examples
values input:
START VALUES machine_1, test_1, branch_1, changeset_1, 13, 1229477017 1,1.0,page_01 2,2.0,page_02 3,3.0,page_03 4,1.0,page_04 5,2.0,page_05 6,3.0,page_06 7,1.0,page_07 8,2.0,page_08 9,3.0,page_09 10,1.0,page_10 11,2.0,page_11 12,3.0,page_12 END
response:
Content-type: text/plain
RETURN\ttest_1\tgraph.html#type=series&tests=[{"test":45,"branch":3455,"machine":234,"testrun"=6667}]
RETURN\ttest_1\t2.00\tgraph.html#tests=[{"test":45,"branch":3455,"machine":234}]
average input:
START AVERAGE machine_1, test_1, branch_1, changeset_1, 13, 1229477017 2.0 END
response:
Content-type: text/plain
RETURN\ttest_1\t2.00\tgraph.html#tests=[{"test":45,"branch":3455,"machine":234}]
browser_output.txt
The data is harvested from browser_output.txt:
__start_tp_report _x_x_mozilla_page_load,4070.909090909091,NaN,NaN _x_x_mozilla_page_load_details,avgmedian|4070.909090909091|average|4070.73|minimum|NaN|maximum|NaN|stddev|NaN |i|pagename|median|mean|min|max|runs| |0;gearflowers.svg;162.5;163;162;226;226;165;163;162;162 |1;composite-scale.svg;77;77.25;77;115;115;77;77;78;77 |2;composite-scale-opacity.svg;31.5;31.75;30;62;62;31;34;30;32 |3;composite-scale-rotate.svg;31;31;29;60;60;29;32;33;30 |4;composite-scale-rotate-opacity.svg;31;31.5;29;36;35;31;31;29;36 |5;hixie-001.xml;15065;15063.75;15059;15086;15059;15065;15065;15066;15086 |6;hixie-002.xml;15064.5;15060.5;15047;15070;15070;15064;15066;15065;15047 |7;hixie-003.xml;5038;5038.75;5037;5054;5042;5037;5037;5054;5039 |8;hixie-004.xml;5081.5;5081.5;5079;5087;5087;5084;5079;5084;5079 |9;hixie-005.xml;6369.5;6367.5;6349;6405;6362;6405;6377;6382;6349 |10;hixie-006.xml;9270;9276;9239;9342;9239;9325;9278;9342;9262 |11;hixie-007.xml;3623.5;3619.25;3601;3653;3627;3629;3653;3601;3620 __end_tp_report __start_cc_report _x_x_mozilla_cycle_collect,1137 __end_cc_report __startTimestamp1327556458940__endTimestamp __startBeforeLaunchTimestamp1327556130230__endBeforeLaunchTimestamp __startAfterTerminationTimestamp1327556459158__endAfterTerminationTimestamp