grahamjenson / ger_rethinkdb_esm Goto Github PK
View Code? Open in Web Editor NEWAn Event Store Manager for Good Enough Recommendations (GER) using RethinkDB
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modeneisger_rethinkdb_esm's Issues
remove filter filter((row) -> row("thing").ne(thing)) from thing_neighbourhood
The following query can be optimized by replacing the filter .ne() and use only indexes on the search.
So this query 40ms round-trip time
return r.table("default_events").getAll([var_3256("person"), var_3257("action")], {
index: "person_action"
}).filter(function(var_3258) {
return var_3258("thing").ne("a")
})
Will be better if we make use of a composed index person_action_thing and use between with union, leaving rightBound and leftBound open.
23ms round-trip time
r.db("hapiger_it").table("default_events").between(["p1", "view", r.minval], ["p1", "view", "a"], {
index: "person_action_thing",
rightBound: "open"
}).union(r.db("hapiger_it").table("default_events").between(["p1", "view", "a"], ["p1", "view", r.maxval], {
index: "person_action_thing",
leftBound: "open"
}))
Actual Query Profile
40ms round-trip time
[
{
"description": "Evaluating filter.",
"duration(ms)": 0.508081,
"sub_tasks": [
{
"description": "Evaluating get_all.",
"duration(ms)": 0.494773,
"sub_tasks": [
{
"description": "Evaluating table.",
"duration(ms)": 0.456685,
"sub_tasks": [
{
"description": "Evaluating db.",
"duration(ms)": 0.065233,
"sub_tasks": [
{
"description": "Evaluating datum.",
"duration(ms)": 0.000765,
"sub_tasks": []
}
]
},
{
"description": "Evaluating datum.",
"duration(ms)": 0.000254,
"sub_tasks": []
}
]
},
{
"description": "Evaluating datum.",
"duration(ms)": 0.000995,
"sub_tasks": []
},
{
"description": "Evaluating make_array.",
"duration(ms)": 0.00856,
"sub_tasks": [
{
"description": "Evaluating datum.",
"duration(ms)": 0.000378,
"sub_tasks": []
},
{
"description": "Evaluating elements in make_array.",
"mean_duration(ms)": 0.000615,
"n_samples": 1
}
]
}
]
},
{
"description": "Evaluating func.",
"duration(ms)": 0.001403,
"sub_tasks": []
}
]
},
{
"description": "Perform read.",
"duration(ms)": 0.713381,
"sub_tasks": [
{
"parallel_tasks": [
[
{
"description": "Perform read on shard.",
"duration(ms)": 0.239102,
"sub_tasks": [
{
"description": "Do range scan on secondary index.",
"duration(ms)": 0.079994,
"sub_tasks": []
}
]
}
],
[
{
"description": "Perform read on shard.",
"duration(ms)": 0.185966,
"sub_tasks": [
{
"description": "Do range scan on secondary index.",
"duration(ms)": 0.02277,
"sub_tasks": []
}
]
}
],
[
{
"description": "Perform read on shard.",
"duration(ms)": 0.136859,
"sub_tasks": [
{
"description": "Do range scan on secondary index.",
"duration(ms)": 0.024292,
"sub_tasks": []
}
]
}
],
[
{
"description": "Perform read on shard.",
"duration(ms)": 0.187845,
"sub_tasks": [
{
"description": "Do range scan on secondary index.",
"duration(ms)": 0.021955,
"sub_tasks": []
}
]
}
],
[
{
"description": "Perform read on shard.",
"duration(ms)": 0.184639,
"sub_tasks": [
{
"description": "Do range scan on secondary index.",
"duration(ms)": 0.022803,
"sub_tasks": []
}
]
}
],
[
{
"description": "Perform read on shard.",
"duration(ms)": 0.137011,
"sub_tasks": [
{
"description": "Do range scan on secondary index.",
"duration(ms)": 0.024401,
"sub_tasks": []
}
]
}
],
[
{
"description": "Perform read on shard.",
"duration(ms)": 0.182047,
"sub_tasks": [
{
"description": "Do range scan on secondary index.",
"duration(ms)": 0.022008,
"sub_tasks": []
}
]
}
],
[
{
"description": "Perform read on shard.",
"duration(ms)": 0.187839,
"sub_tasks": [
{
"description": "Do range scan on secondary index.",
"duration(ms)": 0.022024,
"sub_tasks": []
}
]
}
]
]
}
]
}
]
Optimized Query Profile
23ms round-trip time
[
{
"description": "Evaluating filter.",
"duration(ms)": 0.086508,
"sub_tasks": [
{
"description": "Evaluating get_all.",
"duration(ms)": 0.080609,
"sub_tasks": [
{
"description": "Evaluating table.",
"duration(ms)": 0.068299,
"sub_tasks": [
{
"description": "Evaluating db.",
"duration(ms)": 0.056185,
"sub_tasks": [
{
"description": "Evaluating datum.",
"duration(ms)": 0.000439,
"sub_tasks": []
}
]
},
{
"description": "Evaluating datum.",
"duration(ms)": 0.000251,
"sub_tasks": []
}
]
},
{
"description": "Evaluating datum.",
"duration(ms)": 0.000224,
"sub_tasks": []
},
{
"description": "Evaluating make_array.",
"duration(ms)": 0.003561,
"sub_tasks": [
{
"description": "Evaluating datum.",
"duration(ms)": 0.000165,
"sub_tasks": []
},
{
"description": "Evaluating elements in make_array.",
"mean_duration(ms)": 0.000205,
"n_samples": 1
}
]
}
]
},
{
"description": "Evaluating func.",
"duration(ms)": 0.000915,
"sub_tasks": []
}
]
},
{
"description": "Perform read.",
"duration(ms)": 0.647678,
"sub_tasks": [
{
"parallel_tasks": [
[
{
"description": "Perform read on shard.",
"duration(ms)": 0.17447,
"sub_tasks": [
{
"description": "Do range scan on secondary index.",
"duration(ms)": 0.058303,
"sub_tasks": []
}
]
}
],
[
{
"description": "Perform read on shard.",
"duration(ms)": 0.108219,
"sub_tasks": [
{
"description": "Do range scan on secondary index.",
"duration(ms)": 0.014863,
"sub_tasks": []
}
]
}
],
[
{
"description": "Perform read on shard.",
"duration(ms)": 0.183406,
"sub_tasks": [
{
"description": "Do range scan on secondary index.",
"duration(ms)": 0.022122,
"sub_tasks": []
}
]
}
],
[
{
"description": "Perform read on shard.",
"duration(ms)": 0.179474,
"sub_tasks": [
{
"description": "Do range scan on secondary index.",
"duration(ms)": 0.022076,
"sub_tasks": []
}
]
}
],
[
{
"description": "Perform read on shard.",
"duration(ms)": 0.106296,
"sub_tasks": [
{
"description": "Do range scan on secondary index.",
"duration(ms)": 0.0147,
"sub_tasks": []
}
]
}
],
[
{
"description": "Perform read on shard.",
"duration(ms)": 0.106287,
"sub_tasks": [
{
"description": "Do range scan on secondary index.",
"duration(ms)": 0.014628,
"sub_tasks": []
}
]
}
],
[
{
"description": "Perform read on shard.",
"duration(ms)": 0.174713,
"sub_tasks": [
{
"description": "Do range scan on secondary index.",
"duration(ms)": 0.019082,
"sub_tasks": []
}
]
}
],
[
{
"description": "Perform read on shard.",
"duration(ms)": 0.174785,
"sub_tasks": [
{
"description": "Do range scan on secondary index.",
"duration(ms)": 0.019082,
"sub_tasks": []
}
]
}
]
]
}
]
}
]
implement find_events to accept actions and things
implementation should cover the same as ps impl:
https://github.com/grahamjenson/ger/blob/master/lib/psql_esm.coffee
q = q.where(person: options.person) if options.person
q = q.whereIn('person', options.people) if options.people
q = q.where(action: options.action) if options.action
q = q.whereIn('action', options.actions) if options.actions
q = q.where(thing: options.thing) if options.thing
q = q.whereIn('thing', options.things) if options.things
RethinkDB ESM performance
Investigation:
I'm having a look into this queries, and it seems so far that they can be optimised on Rethinkdb level by adding queries with multiple indexes.
I'm playing with something to optimise the recommend query, this is my first try and it actually reduced the time spent by half:
Before:
r.db("hapiger").table("test_events").getAll("8kGdxrLI07Wz4LJq", {
index: "person"
}).filter(function(var_47) {
return var_47("created_at").le(r.ISO8601("2017-02-04T21:02:23.325Z"))
}).filter(function(var_48) {
return var_48("expires_at").ge(r.ISO8601("2017-02-04T21:02:23.000Z"))
}).filter(function(var_49) {
return r.expr(["view", "buy"]).contains(var_49("action"))
})
.orderBy(r.desc("created_at"))
.limit(5)
31ms round-trip time
After:
r.db("hapiger").table("test_events").between(
["8kGdxrLI07Wz4LJq", r.minval,r.minval,["view", "buy"]], ["8kGdxrLI07Wz4LJq", r.ISO8601("2017-02-05T21:02:23.325Z"),r.ISO8601("2017-02-05T21:02:23.000Z"),["view", "buy"]],
{index: 'person_created_at_expires_at_action'}).orderBy(r.desc("created_at")).limit(5)
15ms round-trip time
I will keep looking into this and submit a PR once I'm sure this will result in performance gains.
Cheers.
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