From 8f5bc40da4a94bba784f48d41698bd6c278c90b8 Mon Sep 17 00:00:00 2001 From: Daniel Mitterdorfer Date: Thu, 6 Jul 2017 13:16:34 +0200 Subject: [PATCH] Visualize benchmark results with Kibana With this commit we embed Kibana dashboards for nightly and release benchmarks. As we want to gather further feedback we did not yet remove the old functionality based on dygraphs (this will happen in #33). Closes #23 Closes #9 Closes #11 Closes #12 --- external/pages/default/geonames/index.html | 219 +++-------------- external/pages/default/geopoint/index.html | 202 +++------------ external/pages/default/logging/index.html | 188 +++----------- external/pages/default/nested/index.html | 176 +++---------- external/pages/default/nyc_taxis/index.html | 188 +++----------- external/pages/default/percolator/index.html | 188 +++----------- external/pages/default/pmc/index.html | 203 +++------------ generate_release_charts.py | 246 +++++++++++++++++++ 8 files changed, 445 insertions(+), 1165 deletions(-) create mode 100644 generate_release_charts.py diff --git a/external/pages/default/geonames/index.html b/external/pages/default/geonames/index.html index 669bdf0356d25..6047d9e6a7976 100644 --- a/external/pages/default/geonames/index.html +++ b/external/pages/default/geonames/index.html @@ -26,55 +26,26 @@ + + @@ -103,20 +79,8 @@
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The "nested" data set contains questions and answers from Stack Overflow.

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The benchmarks are run with a larger heap of 4GB. For more details please refer to the - nested track specification and have a - look at our benchmarking methodology).

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- - - diff --git a/external/pages/default/nyc_taxis/index.html b/external/pages/default/nyc_taxis/index.html index a920bdf926bcc..e0c090448a36d 100644 --- a/external/pages/default/nyc_taxis/index.html +++ b/external/pages/default/nyc_taxis/index.html @@ -26,48 +26,26 @@ + + @@ -100,19 +83,8 @@
  • Geopoint
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    The NYC taxi data set contains the rides that have been performed in yellow taxis in New York in 2015 and have been provided - by the NYC Taxi and Limousine Commission. We use - it to evaluate the performance of Elasticsearch for structured data. We run the following variation (which we call - "challenge" in Rally):

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    • Append Sorted: Indexes the whole document corpus in an index sorted by pickup_datetime field in descending order (most recent first) - and using a setup that will lead to a larger indexing throughput than the default settings and produce a smaller index (higher compression rate). - Document ids are unique so all index operations are append only. -
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    The benchmarks are run either for an out of the box configuration of Elasticsearch but with a larger heap of 4GB. For more - details please refer to the NYC taxis - track specification and have a look at our benchmarking methodology).

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    - - - diff --git a/external/pages/default/percolator/index.html b/external/pages/default/percolator/index.html index 05e6b2e138511..e3443c55e79ad 100644 --- a/external/pages/default/percolator/index.html +++ b/external/pages/default/percolator/index.html @@ -26,51 +26,26 @@ + + @@ -102,19 +81,8 @@
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      The purpose of this benchmark is to evaluate the performance of percolator queries in Elasticsearch. As we are mainly - interested in search performance we don't run a lot of variations of indexing as we do in the other benchmarks but only - append documents and also run on a 4 GB heap (the default settings don't work for this benchmark). For more details please - refer to the percolator track - specification and have a look at our benchmarking methodology).

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      - - - diff --git a/external/pages/default/pmc/index.html b/external/pages/default/pmc/index.html index 1d53c78946091..3df03b49ef3af 100644 --- a/external/pages/default/pmc/index.html +++ b/external/pages/default/pmc/index.html @@ -26,51 +26,26 @@ + + @@ -102,19 +82,8 @@
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      Overview

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      The PMC data set contains scientific papers from PubMed Central ® (PMC). - We use it to evaluate the performance of Elasticsearch for full-text content. We run the following variations (which we call - "challenges" in Rally):

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      • Append: Indexes the whole document corpus using Elasticsearch default settings. We only adjust the - number of replicas as we benchmark a single node cluster and Rally will only start the benchmark if the cluster turns - green. Document ids are unique so all index operations are append only. After that a couple of queries are run in - parallel by multiple clients. -
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      • Append Sorted: Indexes the whole document corpus in an index sorted by timestamp field in descending - order (most recent first). Document ids are unique so all index operations are append only. -
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      • Id Conflicts: Indexes the whole document corpus using a setup that will lead to a larger indexing - throughput than the default settings. Rally will produce duplicate ids in 25% of all documents (not configurable) so we - can simulate a scenario with appends most of the time and some updates in between. -
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      The benchmarks are run either for an out of the box configuration of Elasticsearch ("default settings") or with a larger heap - of 4GB ("4g heap"). For more details please refer to the PMC track specification and have a - look at our benchmarking methodology).

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      - - - diff --git a/generate_release_charts.py b/generate_release_charts.py new file mode 100644 index 0000000000000..a2b35b6d0284e --- /dev/null +++ b/generate_release_charts.py @@ -0,0 +1,246 @@ +import uuid +import json +import os + +all_tracks_and_challenges = { + "nested": [ + ["nested-search-challenge", "4gheap"], + ], + "geonames": [ + ["append-no-conflicts", "defaults"], + ["append-no-conflicts-index-only", "4gheap"], + ["append-sorted-no-conflicts", "4gheap"], + ["append-fast-with-conflicts", "4gheap"], + ["append-no-conflicts-index-only-1-replica", "two_nodes"], + ], + "percolator": [ + ["append-no-conflicts", "4gheap"], + ], + "geopoint": [ + ["append-no-conflicts", "defaults"], + ["append-no-conflicts-index-only", "4gheap"], + ["append-fast-with-conflicts", "4gheap"], + ["append-no-conflicts-index-only-1-replica", "two_nodes"], + ], + "pmc": [ + ["append-no-conflicts-index-only", "defaults"], + ["append-no-conflicts", "4gheap"], + ["append-sorted-no-conflicts", "4gheap"], + ["append-fast-with-conflicts", "4gheap"], + ["append-no-conflicts-index-only-1-replica", "two_nodes"], + ], + "nyc_taxis": [ + ["append-no-conflicts", "4gheap"], + ["append-sorted-no-conflicts-index-only", "4gheap"], + ], + "logging": [ + ["append-no-conflicts-index-only", "defaults"], + ["append-no-conflicts", "4gheap"], + ["append-sorted-no-conflicts", "4gheap"], + ], + "noaa": [ + ["append-no-conflicts", "defaults"] + ] +} + +defaults = { + "geonames": ("append-no-conflicts", "defaults"), + "percolator": ("append-no-conflicts", "4gheap"), + "geopoint": ("append-no-conflicts", "defaults"), + "pmc": ("append-no-conflicts", "4gheap"), + "nyc_taxis": ("append-no-conflicts", "4gheap"), + "nested": ("nested-search-challenge", "4gheap"), + "logging": ("append-no-conflicts", "4gheap"), + "noaa": ("append-no-conflicts", "defaults"), +} + + +def challenges(track_name): + user_home = os.getenv("HOME") + contents = open("%s/.rally/benchmarks/tracks/default/%s/challenges/default.json" % (user_home, track_name)).read() + contents = "[%s]" % contents + return json.loads(contents) + + +def find_challenge(all_challenges, name): + for c in all_challenges: + if c["name"] == name: + return c + return None + + +def generate_index_ops(): + def tracks_for_index(): + all_tracks = [] + for track, challenges_cars in all_tracks_and_challenges.items(): + challenges_of_track = challenges(track) + cci = [] + for cc in challenges_cars: + challenge = cc[0] + car = cc[1] + index_op = find_challenge(challenges_of_track, challenge)["schedule"][0]["operation"] + cci.append((challenge, car, index_op)) + all_tracks.append((track, cci)) + return all_tracks + + structures = [] + for track, cci in tracks_for_index(): + title = "release-%s-indexing-throughput" % track + + filters = "" + for idx, item in enumerate(cci): + challenge, car, index_op = item + if idx > 0: + filters = filters + "," + label = "%s-%s" % (challenge, car) + filters = filters + "{\"input\":{\"query\":{\"query_string\":{\"analyze_wildcard\":true,\"query\":\"operation:%s AND challenge:%s AND car:%s\"}}},\"label\":\"%s\"}" % (index_op, challenge, car, label) + + s = { + "_id": str(uuid.uuid4()), + "_type": "visualization", + "_source": { + "title": title, + "visState": "{\"aggs\":[{\"enabled\":true,\"id\":\"1\",\"params\":{\"customLabel\":\"Median Indexing Throughput [docs/s]\",\"field\":\"value.median\",\"percents\":[50]},\"schema\":\"metric\",\"type\":\"median\"}," + "{\"enabled\":true,\"id\":\"2\",\"params\":{\"field\":\"distribution-version\",\"order\":\"asc\",\"orderBy\":\"_term\",\"size\":10},\"schema\":\"segment\",\"type\":\"terms\"},{\"enabled\":true,\"id\":\"3\",\"params\":" + "{\"field\":\"user-tag\",\"order\":\"desc\",\"orderBy\":\"_term\",\"size\":10},\"schema\":\"group\",\"type\":\"terms\"},{\"enabled\":true,\"id\":\"4\",\"params\":" + "{\"filters\":[%s]" + ",\"row\":true},\"schema\":\"split\",\"type\":\"filters\"}],\"listeners\":{},\"params\":{\"addLegend\":true," + "\"addTimeMarker\":false,\"addTooltip\":true,\"categoryAxes\":[{\"id\":\"CategoryAxis-1\",\"labels\":{\"show\":true," + "\"truncate\":100},\"position\":\"bottom\",\"scale\":{\"type\":\"linear\"},\"show\":true,\"style\":{}," + "\"title\":{\"text\":\"distribution-version: Ascending\"},\"type\":\"category\"}],\"defaultYExtents\":false," + "\"drawLinesBetweenPoints\":true,\"grid\":{\"categoryLines\":false,\"style\":{\"color\":\"#eee\"}}," + "\"interpolate\":\"linear\",\"legendPosition\":\"right\",\"radiusRatio\":9,\"scale\":\"linear\"," + "\"seriesParams\":[{\"data\":{\"id\":\"1\",\"label\":\"Median Indexing Throughput [docs/s]\"}," + "\"drawLinesBetweenPoints\":true,\"mode\":\"stacked\",\"show\":\"true\",\"showCircles\":true,\"type\":" + "\"histogram\",\"valueAxis\":\"ValueAxis-1\"}],\"setYExtents\":false,\"showCircles\":true,\"times\":[]," + "\"valueAxes\":[{\"id\":\"ValueAxis-1\",\"labels\":{\"filter\":false,\"rotate\":0,\"show\":true,\"truncate\":100}," + "\"name\":\"LeftAxis-1\",\"position\":\"left\",\"scale\":{\"mode\":\"normal\",\"type\":\"linear\"},\"show\":true," + "\"style\":{},\"title\":{\"text\":\"Median Indexing Throughput [docs/s]\"},\"type\":\"value\"}]},\"title\":\"%s\",\"type\":\"histogram\"}" % (filters, title), + "uiStateJSON": "{\"vis\":{\"legendOpen\":true}}", + "description": "", + "version": 1, + "kibanaSavedObjectMeta": { + "searchSourceJSON": "{\"index\":\"rally-results-*\",\"query\":{\"query_string\":{\"analyze_wildcard\":true,\"query\":\"environment:release AND active:true AND track:%s AND name:throughput\"}},\"filter\":[]}" % track + } + } + } + structures.append(s) + + return structures + + +def default_tracks(): + all_tracks = [] + for track, challenge_car in defaults.items(): + challenge, car = challenge_car + default_challenge = challenges(track)[0] + # default challenge is usually the first one. No need for complex logic + assert default_challenge["default"] + # filter queries + queries = [t["operation"] for t in default_challenge["schedule"] if + not (t["operation"].startswith("index") or t["operation"] in ["force-merge", "node-stats"])] + all_tracks.append((track, challenge, car, queries)) + + return all_tracks + + +def generate_queries(): + # output JSON structures + structures = [] + for track, challenge, car, queries in default_tracks(): + for q in queries: + title = "release-%s-%s-p99-latency" % (track, q) + label = "Query Latency [ms]" + metric = "service_time" + + s = { + "_id": str(uuid.uuid4()), + "_type": "visualization", + "_source": { + "title": title, + "visState": "{\"title\":\"%s\",\"type\":\"histogram\",\"params\":{\"addLegend\":true,\"addTimeMarker\":false,\"addTooltip\":true,\"" + "categoryAxes\":[{\"id\":\"CategoryAxis-1\",\"labels\":{\"show\":true,\"truncate\":100},\"position\":\"bottom\",\"" + "scale\":{\"type\":\"linear\"},\"show\":true,\"style\":{},\"title\":{\"text\":\"distribution-version: Ascending\"},\"type\":\"category\"}]," + "\"defaultYExtents\":false,\"drawLinesBetweenPoints\":true,\"grid\":{\"categoryLines\":false,\"style\":{\"color\":\"#eee\"}}," + "\"interpolate\":\"linear\",\"legendPosition\":\"right\",\"radiusRatio\":9,\"scale\":\"linear\",\"" + "seriesParams\":[{\"data\":{\"id\":\"1\",\"label\":\"%s\"},\"drawLinesBetweenPoints\":true," + "\"mode\":\"stacked\",\"show\":\"true\",\"showCircles\":true,\"type\":\"histogram\",\"valueAxis\":\"ValueAxis-1\"}]," + "\"setYExtents\":false,\"showCircles\":true,\"times\":[],\"valueAxes\":[{\"id\":\"ValueAxis-1\"," + "\"labels\":{\"filter\":false,\"rotate\":0,\"show\":true,\"truncate\":100},\"name\":\"LeftAxis-1\"," + "\"position\":\"left\",\"scale\":{\"mode\":\"normal\",\"type\":\"linear\"},\"show\":true,\"style\":{}," + "\"title\":{\"text\":\"%s\"},\"type\":\"value\"}]},\"aggs\":[{\"id\":\"1\"," + "\"enabled\":true,\"type\":\"median\",\"schema\":\"metric\",\"params\":{\"field\":\"value.99_0\"," + "\"percents\":[50],\"customLabel\":\"%s\"}},{\"id\":\"2\",\"enabled\":true,\"type\":\"terms\",\"schema\":" + "\"segment\",\"params\":{\"field\":\"distribution-version\",\"size\":10,\"order\":\"asc\"," + "\"orderBy\":\"_term\"}},{\"id\":\"3\",\"enabled\":true,\"type\":\"terms\",\"schema\":\"group\"," + "\"params\":{\"field\":\"user-tag\",\"size\":10,\"order\":\"desc\",\"orderBy\":\"_term\"}}]," + "\"listeners\":{}}" % (title, label, label, label), + "uiStateJSON": "{}", + "description": "", + "version": 1, + "kibanaSavedObjectMeta": { + "searchSourceJSON": "{\"index\":\"rally-results-*\",\"query\":{\"query_string\":" + "{\"query\":\"environment:release AND active:true AND track:%s AND name:%s AND operation:%s AND challenge:%s AND car:%s\",\"analyze_wildcard\":true}},\"filter\":[]}" % ( + track, metric, q, challenge, car) + } + } + } + structures.append(s) + return structures + + +def generate_io(): + # output JSON structures + structures = [] + for track, challenge, car, queries in default_tracks(): + title = "release-%s-io" % track + + s = { + "_id": str(uuid.uuid4()), + "_type": "visualization", + "_source": { + "title": title, + "visState": "{\"title\":\"%s\",\"type\":\"histogram\",\"params\":{\"addLegend\":true,\"addTimeMarker\":false,\"addTooltip\":true,\"categoryAxes\":[{\"id\":\"CategoryAxis-1\",\"labels\":{\"show\":true,\"truncate\":100},\"position\":\"bottom\",\"scale\":{\"type\":\"linear\"},\"show\":true,\"style\":{},\"title\":{\"text\":\"filters\"},\"type\":\"category\"}],\"defaultYExtents\":false,\"drawLinesBetweenPoints\":true,\"grid\":{\"categoryLines\":false,\"style\":{\"color\":\"#eee\"}},\"interpolate\":\"linear\",\"legendPosition\":\"right\",\"radiusRatio\":9,\"scale\":\"linear\",\"seriesParams\":[{\"data\":{\"id\":\"1\",\"label\":\"[Bytes]\"},\"drawLinesBetweenPoints\":true,\"mode\":\"stacked\",\"show\":\"true\",\"showCircles\":true,\"type\":\"histogram\",\"valueAxis\":\"ValueAxis-1\"}],\"setYExtents\":false,\"showCircles\":true,\"times\":[],\"valueAxes\":[{\"id\":\"ValueAxis-1\",\"labels\":{\"filter\":false,\"rotate\":0,\"show\":true,\"truncate\":100},\"name\":\"LeftAxis-1\",\"position\":\"left\",\"scale\":{\"mode\":\"normal\",\"type\":\"linear\"},\"show\":true,\"style\":{},\"title\":{\"text\":\"[Bytes]\"},\"type\":\"value\"}]},\"aggs\":[{\"id\":\"1\",\"enabled\":true,\"type\":\"median\",\"schema\":\"metric\",\"params\":{\"field\":\"value.single\",\"percents\":[50],\"customLabel\":\"[Bytes]\"}},{\"id\":\"2\",\"enabled\":true,\"type\":\"filters\",\"schema\":\"segment\",\"params\":{\"filters\":[{\"input\":{\"query\":{\"query_string\":{\"analyze_wildcard\":true,\"query\":\"name:index_size\"}}},\"label\":\"Index size\"},{\"input\":{\"query\":{\"query_string\":{\"analyze_wildcard\":true,\"query\":\"name:bytes_written\"}}},\"label\":\"Bytes written\"}]}},{\"id\":\"3\",\"enabled\":true,\"type\":\"terms\",\"schema\":\"split\",\"params\":{\"field\":\"distribution-version\",\"size\":10,\"order\":\"asc\",\"orderBy\":\"_term\",\"row\":false}},{\"id\":\"4\",\"enabled\":true,\"type\":\"terms\",\"schema\":\"group\",\"params\":{\"field\":\"user-tag\",\"size\":5,\"order\":\"desc\",\"orderBy\":\"_term\"}}],\"listeners\":{}}" % title, + "uiStateJSON": "{}", + "description": "", + "version": 1, + "kibanaSavedObjectMeta": { + "searchSourceJSON": "{\"index\":\"rally-results-*\",\"query\":{\"query_string\":{\"query\":\"environment:release AND active:true AND track:%s AND challenge:%s AND car:%s\",\"analyze_wildcard\":true}},\"filter\":[]}" % (track, challenge, car) + } + } + } + structures.append(s) + + return structures + + +def generate_gc(): + structures = [] + for track, challenge, car, queries in default_tracks(): + title = "release-%s-gc" % track + s = { + "_id": str(uuid.uuid4()), + "_type": "visualization", + "_source": { + "title": title, + "visState": "{\"title\":\"%s\",\"type\":\"histogram\",\"params\":{\"addLegend\":true,\"addTimeMarker\":false,\"addTooltip\":true,\"categoryAxes\":[{\"id\":\"CategoryAxis-1\",\"labels\":{\"show\":true,\"truncate\":100},\"position\":\"bottom\",\"scale\":{\"type\":\"linear\"},\"show\":true,\"style\":{},\"title\":{\"text\":\"filters\"},\"type\":\"category\"}],\"defaultYExtents\":false,\"drawLinesBetweenPoints\":true,\"grid\":{\"categoryLines\":false,\"style\":{\"color\":\"#eee\"}},\"interpolate\":\"linear\",\"legendPosition\":\"right\",\"radiusRatio\":9,\"scale\":\"linear\",\"seriesParams\":[{\"data\":{\"id\":\"1\",\"label\":\"Total GC Duration [ms]\"},\"drawLinesBetweenPoints\":true,\"mode\":\"stacked\",\"show\":\"true\",\"showCircles\":true,\"type\":\"histogram\",\"valueAxis\":\"ValueAxis-1\"}],\"setYExtents\":false,\"showCircles\":true,\"times\":[],\"valueAxes\":[{\"id\":\"ValueAxis-1\",\"labels\":{\"filter\":false,\"rotate\":0,\"show\":true,\"truncate\":100},\"name\":\"LeftAxis-1\",\"position\":\"left\",\"scale\":{\"mode\":\"normal\",\"type\":\"linear\"},\"show\":true,\"style\":{},\"title\":{\"text\":\"Total GC Duration [ms]\"},\"type\":\"value\"}]},\"aggs\":[{\"id\":\"1\",\"enabled\":true,\"type\":\"median\",\"schema\":\"metric\",\"params\":{\"field\":\"value.single\",\"percents\":[50],\"customLabel\":\"Total GC Duration [ms]\"}},{\"id\":\"2\",\"enabled\":true,\"type\":\"filters\",\"schema\":\"segment\",\"params\":{\"filters\":[{\"input\":{\"query\":{\"query_string\":{\"query\":\"name:young_gc_time\",\"analyze_wildcard\":true}}},\"label\":\"Young GC\"},{\"input\":{\"query\":{\"query_string\":{\"query\":\"name:old_gc_time\",\"analyze_wildcard\":true}}},\"label\":\"Old GC\"}]}},{\"id\":\"3\",\"enabled\":true,\"type\":\"terms\",\"schema\":\"split\",\"params\":{\"field\":\"distribution-version\",\"size\":10,\"order\":\"asc\",\"orderBy\":\"_term\",\"row\":false}},{\"id\":\"4\",\"enabled\":true,\"type\":\"terms\",\"schema\":\"group\",\"params\":{\"field\":\"user-tag\",\"size\":5,\"order\":\"desc\",\"orderBy\":\"_term\"}}],\"listeners\":{}}" % title, + "uiStateJSON": "{}", + "description": "", + "version": 1, + "kibanaSavedObjectMeta": { + "searchSourceJSON": "{\"index\":\"rally-results-*\",\"query\":{\"query_string\":{\"query\":\"environment:release AND active:true AND track:%s AND challenge:%s AND car:%s\",\"analyze_wildcard\":true}},\"filter\":[]}" % (track, challenge, car) + } + } + } + structures.append(s) + + return structures + + +def main(): + structures = generate_index_ops() + generate_queries() + generate_io() + generate_gc() + print(json.dumps(structures, indent=4)) + + +if __name__ == '__main__': + main()