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Update to speed comparison for Redis vs PostgreSQL storing blobs of JSON

30 September 2019 2 comments   PostgreSQL, Django, Python, Web Performance, Nginx, Redis


Last week, I blogged about "How much faster is Redis at storing a blob of JSON compared to PostgreSQL?". Judging from a lot of comments, people misinterpreted this. (By the way, Redis is persistent). It's no surprise that Redis is faster.

However, it's a fact that I have do have a lot of blobs stored and need to present them via the web API as fast as possible. It's rare that I want to do relational or batch operations on the data. But Redis isn't a slam dunk for simple retrieval because I don't know if I trust its integrity with the 3GB worth of data that I both don't want to lose and don't want to load all into RAM.

But is it entirely wrong to look at WHICH database to get the best speed?

Reviewing this corner of Song Search helped me rethink this. PostgreSQL is, in my view, a better database for storing stuff. Redis is faster for individual lookups. But you know what's even faster? Nginx

Nginx??

The way the application works is that a React web app is requesting the Amazon product data for the sake of presenting an appropriate affiliate link. This is done by the browser essentially doing:

const response = await fetch('https://songsear.ch/api/song/5246889/amazon');

Internally, in the app, what it does is that it looks this up, by ID, on the AmazonAffiliateLookup ORM model. Suppose it wasn't there in the PostgreSQL, it uses the Amazon Affiliate Product Details API, to look it up and when the results come in it stores a copy of this in PostgreSQL so we can re-use this URL without hitting rate limits on the Product Details API. Lastly, in a piece of Django view code, it carefully scrubs and repackages this result so that only the fields used by the React rendering code is shipped between the server and the browser. That "scrubbed" piece of data is actually much smaller. Partly because it limits the results to the first/best match and it deletes a bunch of things that are never needed such as ProductTypeName, Studio, TrackSequence etc. The proportion is roughly 23x. I.e. of the 3GB of JSON blobs stored in PostgreSQL only 130MB is ever transported from the server to the users.

Again, Nginx?

Nginx has a built in reverse HTTP proxy cache which is easy to set up but a bit hard to do purges on. The biggest flaw, in my view, is that it's hard to get a handle of how much RAM this it's eating up. Well, if the total possible amount of data within the server is 130MB, then that is something I'm perfectly comfortable to let Nginx handle cache in RAM.

Good HTTP performance benchmarking is hard to do but here's a teaser from my local laptop version of Nginx:

▶ hey -n 10000 -c 10 https://songsearch.local/api/song/1810960/affiliate/amazon-itunes

Summary:
  Total:    0.9882 secs
  Slowest:  0.0279 secs
  Fastest:  0.0001 secs
  Average:  0.0010 secs
  Requests/sec: 10119.8265


Response time histogram:
  0.000 [1] |
  0.003 [9752]  |■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■
  0.006 [108]   |
  0.008 [70]    |
  0.011 [32]    |
  0.014 [8] |
  0.017 [12]    |
  0.020 [11]    |
  0.022 [1] |
  0.025 [4] |
  0.028 [1] |


Latency distribution:
  10% in 0.0003 secs
  25% in 0.0006 secs
  50% in 0.0008 secs
  75% in 0.0010 secs
  90% in 0.0013 secs
  95% in 0.0016 secs
  99% in 0.0068 secs

Details (average, fastest, slowest):
  DNS+dialup:   0.0000 secs, 0.0001 secs, 0.0279 secs
  DNS-lookup:   0.0000 secs, 0.0000 secs, 0.0026 secs
  req write:    0.0000 secs, 0.0000 secs, 0.0011 secs
  resp wait:    0.0008 secs, 0.0001 secs, 0.0206 secs
  resp read:    0.0001 secs, 0.0000 secs, 0.0013 secs

Status code distribution:
  [200] 10000 responses

10,000 requests across 10 clients at rougly 10,000 requests per second. That includes doing all the HTTP parsing, WSGI stuff, forming of a SQL or Redis query, the deserialization, the Django JSON HTTP response serialization etc. The cache TTL is controlled by simply setting a Cache-Control HTTP header with something like max-age=86400.

Now, repeated fetches for this are cached at the Nginx level and it means it doesn't even matter how slow/fast the database is. As long as it's not taking seconds, with a long Cache-Control, Nginx can hold on to this in RAM for days or until the whole server is restarted (which is rare).

Conclusion

If you the total amount of data that can and will be cached is controlled, putting it in a HTTP reverse proxy cache is probably order of magnitude faster than messing with chosing which database to use.

How much faster is Redis at storing a blob of JSON compared to PostgreSQL?

28 September 2019 59 comments   Redis, PostgreSQL, Python


tl;dr; Redis is 16 times faster at reading these JSON blobs.

In Song Search when you've found a song, it loads some affiliate links to Amazon.com. (In case you're curious it's earning me lower double-digit dollars per month). To avoid overloading the Amazon Affiliate Product API, after I've queried their API, I store that result in my own database along with some metadata. Then, the next time someone views that song page, it can read from my local database. With me so far?

Example view of affiliate links

The other caveat is that you can't store these lookups locally too long since prices change and/or results change. So if my own stored result is older than a couple of hundred days, I delete it and fetch from the network again. My current implementation uses PostgreSQL (via the Django ORM) to store this stuff. The model looks like this:

class AmazonAffiliateLookup(models.Model, TotalCountMixin):
    song = models.ForeignKey(Song, on_delete=models.CASCADE)
    matches = JSONField(null=True)
    search_index = models.CharField(max_length=100, null=True)
    lookup_seconds = models.FloatField(null=True)
    created = models.DateTimeField(auto_now_add=True, db_index=True)
    modified = models.DateTimeField(auto_now=True)

At the moment this database table is 3GB on disk.

Then, I thought, why not use Redis for this. Then I can use Redis's "natural" expiration by simply setting as expiry time when I store it and then I don't have to worry about cleaning up old stuff at all.

The way I'm using Redis in this project is as a/the cache backend and I have it configured like this:

CACHES = {
    "default": {
        "BACKEND": "django_redis.cache.RedisCache",
        "LOCATION": REDIS_URL,
        "TIMEOUT": config("CACHE_TIMEOUT", 500),
        "KEY_PREFIX": config("CACHE_KEY_PREFIX", ""),
        "OPTIONS": {
            "COMPRESSOR": "django_redis.compressors.zlib.ZlibCompressor",
            "SERIALIZER": "django_redis.serializers.msgpack.MSGPackSerializer",
        },
    }
}

The speed difference

Perhaps unrealistic but I'm doing all this testing here on my MacBook Pro. The connection to Postgres (version 11.4) and Redis (3.2.1) are both on localhost.

Reads

The reads are the most important because hopefully, they happen 10x more than writes as several people can benefit from previous saves.

I changed my code so that it would do a read from both databases and if it was found in both, write down their time in a log file which I'll later summarize. Results are as follows:

PG:
median: 8.66ms
mean  : 11.18ms
stdev : 19.48ms

Redis:
median: 0.53ms
mean  : 0.84ms
stdev : 2.26ms

(310 measurements)

It means, when focussing on the median, Redis is 16 times faster than PostgreSQL at reading these JSON blobs.

Writes

The writes are less important but due to the synchronous nature of my Django, the unlucky user who triggers a look up that I didn't have, will have to wait for the write before the XHR request can be completed. However, when this happens, the remote network call to the Amazon Product API is bound to be much slower. Results are as follows:

PG:
median: 8.59ms
mean  : 8.58ms
stdev : 6.78ms

Redis:
median: 0.44ms
mean  : 0.49ms
stdev : 0.27ms

(137 measurements)

It means, when focussing on the median, Redis is 20 times faster than PostgreSQL at writing these JSON blobs.

Conclusion and discussion

First of all, I'm still a PostgreSQL fan-boy and have no intention of ceasing that. These times are made up of much more than just the individual databases. For example, the PostgreSQL speeds depend on the Django ORM code that makes the SQL and sends the query and then turns it into the model instance. I don't know what the proportions are between that and the actual bytes-from-PG's-disk times. But I'm not sure I care either. The tooling around the database is inevitable mostly and it's what matters to users.

Both Redis and PostgreSQL are persistent and survive server restarts and crashes etc. And you get so many more "batch related" features with PostgreSQL if you need them, such as being able to get a list of the last 10 rows added for some post-processing batch job.

I'm currently using Django's cache framework, with Redis as its backend, and it's a cache framework. It's not meant to be a persistent database. I like the idea that if I really have to I can just flush the cache and although detrimental to performance (temporarily) it shouldn't be a disaster. So I think what I'll do is store these JSON blobs in both databases. Yes, it means roughly 6GB of SSD storage but it also potentially means loading a LOT more into RAM on my limited server. That extra RAM usage pretty much sums of this whole blog post; of course it's faster if you can rely on RAM instead of disk. Now I just need to figure out how RAM I can afford myself for this piece and whether it's worth it.

UPDATE September 29, 2019

I experimented with an optimization of NOT turning the Django ORM query into a model instance for each record. Instead, I did this:

+from dataclasses import dataclass


+@dataclass
+class _Lookup:
+    modified: datetime.datetime
+    matches: list

...

+base_qs = base_qs.values_list("modified", "matches")
-lookup = base_qs.get(song__id=song_id)
+lookup_tuple = base_qs.get(song__id=song_id)
+lookup = _Lookup(*lookup_tuple)

print(lookup.modified)

Basically, let the SQL driver's "raw Python" content come through the Django ORM. The old difference between PostgreSQL and Redis was 16x. The new difference was 14x instead.

Best way to count distinct indexed things in PostgreSQL

21 March 2019 2 comments   PostgreSQL, Django


tl;dr; SELECT COUNT(*) FROM (SELECT DISTINCT my_not_unique_indexed_column FROM my_table) t;

I have a table that looks like this:

songsearch=# \d main_songtexthash
            Table "public.main_songtexthash"
  Column   |           Type           | Collation | Nullable |
-----------+--------------------------+-----------+----------+
 id        | integer                  |           | not null |
 text_hash | character varying(32)    |           | not null |
 created   | timestamp with time zone |           | not null |
 modified  | timestamp with time zone |           | not null |
 song_id   | integer                  |           | not null |
Indexes:
    "main_songtexthash_pkey" PRIMARY KEY, btree (id)
    "main_songtexthash_song_id_key" UNIQUE CONSTRAINT, btree (song_id)
    "main_songtexthash_text_hash_c2771f1f" btree (text_hash)
    "main_songtexthash_text_hash_c2771f1f_like" btree (text_hash varchar_pattern_ops)
Foreign-key constraints:
    ...snip...

And the data looks something like this:

songsearch=# select text_hash, song_id from main_songtexthash limit 10;
            text_hash             | song_id
----------------------------------+---------
 6f98e1945e64353bead9d6ab47a7f176 | 2565031
 0c6662363aa4a340fea5efa24c98db76 |  486091
 a25af539b183cbc338409c7acecc6828 |     212
 5aaf561b38c251e7d863aae61fe1363f | 2141077
 6a221df60f7cbb8a4e604f87c9e3aec0 |  245186
 d2a0b5b3b33cdf5e03a75cfbf4963a6f | 1453382
 95c395dd78679120269518b19187ca80 |  981402
 8ab19b32b3be2d592aa69e4417b732cd |  616848
 8ab19b32b3be2d592aa69e4417b732cd |  243393
 01568f1f57aeb7a97e2544978fc93b4c |     333
(10 rows)

If you look carefully, you'll notice that every song_id has a different text_hash except two of them.
Song IDs 616848 and 243393 both have the same text_hash of value 8ab19b32b3be2d592aa69e4417b732cd.

Also, if you imagine this table only has 10 rows, you could quickly and easily conclude that there are 10 different song_id but 9 different distinct text_hash. However, how do you do this counting if the tables are large??

The Wrong Way

songsearch=# select count(distinct text_hash) from main_songtexthash;
  count
---------
 1825983
(1 row)

And the explanation and cost analysis is:

songsearch=# explain analyze select count(distinct text_hash) from main_songtexthash;
                                                             QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------
 Aggregate  (cost=44942.09..44942.10 rows=1 width=8) (actual time=40029.225..40029.226 rows=1 loops=1)
   ->  Seq Scan on main_songtexthash  (cost=0.00..40233.87 rows=1883287 width=33) (actual time=0.029..193.653 rows=1879521 loops=1)
 Planning Time: 0.059 ms
 Execution Time: 40029.250 ms
(4 rows)

Oh noes! A Sec Scan! Run!

The Right Way

Better explained in this blog post but basically, cutting to the chase, here's how you count on an indexed field:

songsearch=# select count(*) from (select distinct text_hash from main_songtexthash) t;
  count
---------
 1825983
(1 row)

And the explanation and cost analysis is:

songsearch=# explain analyze select count(*) from (select distinct text_hash from main_songtexthash) t;
                                                                                          QUERY PLAN
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 Aggregate  (cost=193871.20..193871.21 rows=1 width=8) (actual time=4894.555..4894.556 rows=1 loops=1)
   ->  Unique  (cost=0.55..172861.54 rows=1680773 width=33) (actual time=0.075..4704.741 rows=1825983 loops=1)
         ->  Index Only Scan using main_songtexthash_text_hash_c2771f1f on main_songtexthash  (cost=0.55..168153.32 rows=1883287 width=33) (actual time=0.074..4132.822 rows=1879521 loops=1)
               Heap Fetches: 1879521
 Planning Time: 0.082 ms
 Execution Time: 4894.581 ms
(6 rows)

Same exact result but ~5s instead of ~40s . I'll take that, thank you very much.

The Django Way

As a bonus: Django is smart. Here's how they do it:

>>> SongTextHash.objects.values('text_hash').distinct().count()
1825983

And, the SQL it generates to make that count looks very familiar:

SELECT COUNT(*) FROM (SELECT DISTINCT "main_songtexthash"."text_hash" AS Col1 FROM "main_songtexthash") subquery

Conclusion

Django ORM optimization story on selecting the least possible

22 February 2019 16 comments   PostgreSQL, Python, Django, Web development


This an optimization story that should not surprise anyone using the Django ORM. But I thought I'd share because I have numbers now! The origin of this came from a real requirement. For a given parent model, I'd like to extract the value of the name column of all its child models, and the turn all these name strings into 1 MD5 checksum string.

Variants

The first attempted looked like this:

artist = Artist.objects.get(name="Bad Religion")
names = []
for song in Song.objects.filter(artist=artist):
    names.append(song.name)
return hashlib.md5("".join(names).encode("utf-8")).hexdigest()

The SQL used to generate this is as follows:

SELECT "main_song"."id", "main_song"."artist_id", "main_song"."name", 
"main_song"."text", "main_song"."language", "main_song"."key_phrases", 
"main_song"."popularity", "main_song"."text_length", "main_song"."metadata", 
"main_song"."created", "main_song"."modified", 
"main_song"."has_lastfm_listeners", "main_song"."has_spotify_popularity" 
FROM "main_song" WHERE "main_song"."artist_id" = 22729;

Clearly, I don't need anything but just the name column, version 2:

artist = Artist.objects.get(name="Bad Religion")
names = []
for song in Song.objects.filter(artist=artist).only("name"):
    names.append(song.name)
return hashlib.md5("".join(names).encode("utf-8")).hexdigest()

Now, the SQL used is:

SELECT "main_song"."id", "main_song"."name" 
FROM "main_song" WHERE "main_song"."artist_id" = 22729;

But still, since I don't really need instances of model class Song I can use the .values() method which gives back a list of dictionaries. This is version 3:

names = []
for song in Song.objects.filter(artist=a).values("name"):
    names.append(song["name"])
return hashlib.md5("".join(names).encode("utf-8")).hexdigest()

This time Django figures it doesn't even need the primary key value so it looks like this:

SELECT "main_song"."name" FROM "main_song" WHERE "main_song"."artist_id" = 22729;

Last but not least; there is an even faster one. values_list(). This time it doesn't even bother to map the column name to the value in a dictionary. And since I only need 1 column's value, I can set flat=True. Version 4 looks like this:

names = []
for name in Song.objects.filter(artist=a).values_list("name", flat=True):
    names.append(name)
return hashlib.md5("".join(names).encode("utf-8")).hexdigest()

Same SQL gets used this time as in version 3.

The benchmark

Hopefully this little benchmark script speaks for itself:

from songsearch.main.models import *

import hashlib


def f1(a):
    names = []
    for song in Song.objects.filter(artist=a):
        names.append(song.name)
    return hashlib.md5("".join(names).encode("utf-8")).hexdigest()


def f2(a):
    names = []
    for song in Song.objects.filter(artist=a).only("name"):
        names.append(song.name)
    return hashlib.md5("".join(names).encode("utf-8")).hexdigest()


def f3(a):
    names = []
    for song in Song.objects.filter(artist=a).values("name"):
        names.append(song["name"])
    return hashlib.md5("".join(names).encode("utf-8")).hexdigest()


def f4(a):
    names = []
    for name in Song.objects.filter(artist=a).values_list("name", flat=True):
        names.append(name)
    return hashlib.md5("".join(names).encode("utf-8")).hexdigest()


artist = Artist.objects.get(name="Bad Religion")
print(Song.objects.filter(artist=artist).count())

print(f1(artist) == f2(artist))
print(f2(artist) == f3(artist))
print(f3(artist) == f4(artist))

# Reporting
import time
import random
import statistics

functions = f1, f2, f3, f4
times = {f.__name__: [] for f in functions}

for i in range(500):
    func = random.choice(functions)
    t0 = time.time()
    func(artist)
    t1 = time.time()
    times[func.__name__].append((t1 - t0) * 1000)

for name in sorted(times):
    numbers = times[name]
    print("FUNCTION:", name, "Used", len(numbers), "times")
    print("\tBEST", min(numbers))
    print("\tMEDIAN", statistics.median(numbers))
    print("\tMEAN  ", statistics.mean(numbers))
    print("\tSTDEV ", statistics.stdev(numbers))

I ran this on my PostgreSQL 11.1 on my MacBook Pro with Django 2.1.7. So the database is on localhost.

The results

276
True
True
True
FUNCTION: f1 Used 135 times
    BEST 6.309986114501953
    MEDIAN 7.531881332397461
    MEAN   7.834429211086697
    STDEV  2.03779968066591
FUNCTION: f2 Used 135 times
    BEST 3.039121627807617
    MEDIAN 3.7298202514648438
    MEAN   4.012803678159361
    STDEV  1.8498943539073027
FUNCTION: f3 Used 110 times
    BEST 0.9920597076416016
    MEDIAN 1.4405250549316406
    MEAN   1.5053835782137783
    STDEV  0.3523240470133114
FUNCTION: f4 Used 120 times
    BEST 0.9369850158691406
    MEDIAN 1.3251304626464844
    MEAN   1.4017681280771892
    STDEV  0.3391019435930447

Bar chart

Discussion

I guess the hashlib.md5("".join(names).encode("utf-8")).hexdigest() stuff is a bit "off-topic" but I checked and it's roughly 300 times faster than building up the names list.

It's clearly better to ask less of Python and PostgreSQL to get a better total time. No surprise there. What was interesting was the proportion of these differences. Memorize that and you'll be better equipped if it's worth the hassle of not using the Django ORM in the most basic form.

Also, do take note that this is only relevant in when dealing with many records. The slowest variant (f1) takes, on average, 7 milliseconds.

Summarizing the difference with percentages compared to the fastest variant:

UPDATE Feb 25 2019

James suggested, although a bit "missing the point", that it could be even faster if all the aggregation is pushed into the PostgreSQL server and then the only thing that needs to transfer from PostgreSQL to Python is the final result.

By the way, name column in this particular benchmark, when concatenated into one big string, is ~4KB. So, with variant f5 it only needs to transfer 32 bytes which will/would make a bigger difference if the network latency is higher.

Here's the whole script: https://gist.github.com/peterbe/b2b7ed95d422ab25a65639cb8412e75e

And the results:

276
True
True
True
False
False
FUNCTION: f1 Used 92 times
    BEST 5.928993225097656
    MEDIAN 7.311463356018066
    MEAN   7.594626882801885
    STDEV  2.2027017044658423
FUNCTION: f2 Used 75 times
    BEST 2.878904342651367
    MEDIAN 3.3979415893554688
    MEAN   3.4774907430013022
    STDEV  0.5120246550765524
FUNCTION: f3 Used 88 times
    BEST 0.9310245513916016
    MEDIAN 1.1944770812988281
    MEAN   1.3105544176968662
    STDEV  0.35922655625999383
FUNCTION: f4 Used 71 times
    BEST 0.7879734039306641
    MEDIAN 1.1661052703857422
    MEAN   1.2262606284987758
    STDEV  0.3561764250427344
FUNCTION: f5 Used 90 times
    BEST 0.7929801940917969
    MEDIAN 1.0334253311157227
    MEAN   1.1836051940917969
    STDEV  0.4001442703048186
FUNCTION: f6 Used 84 times
    BEST 0.80108642578125
    MEDIAN 1.1119842529296875
    MEAN   1.2281338373819988
    STDEV  0.37146893005516973

Result: f5 is takes 0.793ms and (the previous "winner") f4 takes 0.788ms.

I'm not entirely sure why f5 isn't faster but I suspect it's because the dataset is too small for it all to matter.

Compare:

songsearch=# explain analyze SELECT "main_song"."name" FROM "main_song" WHERE "main_song"."artist_id" = 22729;
                                                             QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------
 Index Scan using main_song_ca949605 on main_song  (cost=0.43..229.33 rows=56 width=16) (actual time=0.014..0.208 rows=276 loops=1)
   Index Cond: (artist_id = 22729)
 Planning Time: 0.113 ms
 Execution Time: 0.242 ms
(4 rows)

with...

songsearch=# explain analyze SELECT md5(STRING_AGG("main_song"."name", '')) AS "names_hash" FROM "main_song" WHERE "main_song"."artist_id" = 22729;
                                                                QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------------
 Aggregate  (cost=229.47..229.48 rows=1 width=32) (actual time=0.278..0.278 rows=1 loops=1)
   ->  Index Scan using main_song_ca949605 on main_song  (cost=0.43..229.33 rows=56 width=16) (actual time=0.019..0.204 rows=276 loops=1)
         Index Cond: (artist_id = 22729)
 Planning Time: 0.115 ms
 Execution Time: 0.315 ms
(5 rows)

I ran these two SQL statements about 100 times each and recorded their best possible execution times:

1) The plain SELECT - 0.99ms
2) The STRING_AGG - 1.06ms

So that accounts from ~0.1ms difference only! Which kinda matches the results seen above. All in all, I think the dataset is too small to demonstrate this technique. But, considering the chance that the complexity might not be linear with the performance benefit, it's still interesting.

Even though this tangent is a big off-topic, it is often a great idea to push as much work into the database as you can if applicable. Especially if it means you can transfer a lot less data eventually.

How I performance test PostgreSQL locally on macOS

10 December 2018 2 comments   PostgreSQL, MacOSX, Web development


It's weird to do performance analysis of a database you run on your laptop. When testing some app, your local instance probably has 1/1000 the amount of realistic data compared to a production server. Or, you're running a bunch of end-to-end integration tests whose PostgreSQL performance doesn't make sense to measure.

Anyway, if you are doing some performance testing of an app that uses PostgreSQL one great tool to use is pghero. I use it for my side-projects and it gives me such nice insights into slow queries that I'm willing to live with the cost that it is to run it on a production database.

This is more of a brain dump of how I run it locally:

First, you need to edit your postgresql.conf. Even if you used Homebrew to install it, it's not clear where the right config file is. Start psql (on any database) and type this to find out which file is the one:

$ psql kintobench

kintobench=# show config_file;
               config_file
-----------------------------------------
 /usr/local/var/postgres/postgresql.conf
(1 row)

Now, open /usr/local/var/postgres/postgresql.conf and add the following lines:

# Peterbe: From Pghero's configuration help.
shared_preload_libraries = 'pg_stat_statements'
pg_stat_statements.track = all

Now, to restart the server use:

▶ brew services restart postgresql
Stopping `postgresql`... (might take a while)
==> Successfully stopped `postgresql` (label: homebrew.mxcl.postgresql)
==> Successfully started `postgresql` (label: homebrew.mxcl.postgresql)

The next thing you need is pghero itself and it's easy to run in docker. So to start, you need Docker for mac installed. You also need to know the database URL. Here's how I ran it:

docker run -ti -e DATABASE_URL=postgres://peterbe:@host.docker.internal:5432/kintobench -p 8080:8080 ankane/pghero

Duplicate indexes

Note the trick of peterbe:@host.docker.internal because I don't use a password but inside the Docker container it doesn't know my terminal username. And the host.docker.internal is so the Docker container can reach the PostgreSQL installed on the host.

Once that starts up you can go to http://localhost:8080 in a browser and see a listing of all the cumulatively slowest queries. There are other cool features in pghero too that you can immediately benefit from such as hints about unused/redundent database indices.

Hope it helps!

Best EXPLAIN ANALYZE benchmark script

19 April 2018 0 comments   PostgreSQL, Python

https://gist.github.com/peterbe/966effb3f357258ddda5aa8ac385b418


tl;dr; Use best-explain-analyze.py to benchmark a SQL query in Postgres.

I often benchmark SQL by extracting the relevant SQL string, prefix it with EXPLAIN ANALYZE, putting it into a file (e.g. benchmark.sql) and then running psql mydatabase < benchmark.sql. That spits out something like this:

QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------------
 Index Scan using main_song_ca949605 on main_song  (cost=0.43..237.62 rows=1 width=4) (actual time=1.586..1.586 rows=0 loops=1)
   Index Cond: (artist_id = 27451)
   Filter: (((name)::text % 'Facing The Abyss'::text) AND (id <> 2856345))
   Rows Removed by Filter: 170
 Planning time: 3.335 ms
 Execution time: 1.701 ms
(6 rows)

Cool. So you study the steps of the query plan and look for "Seq Scan" and various sub-optimal uses of heaps and indices etc. But often, you really want to just look at the Execution time milliseconds number. Especially if you might have to slightly different SQL queries to compare and contrast.

However, as you might have noticed, the number on the Execution time varies between runs. You might think nothing's changed but Postgres might have warmed up some internal caches or your host might be more busy or less busy. To remedy this, you run the EXPLAIN ANALYZE select ... a couple of times to get a feeling for an average. But there's a much better way!

best-explain-analyze.py

Check this out: best-explain-analyze.py

Download it into your ~/bin/ and chmod +x ~/bin/best-explain-analyze.py. I wrote it just this morning so don't judge!

Now, when you run it it runs that thing 10 times (by default) and reports the best Execution time, its mean and its median. Example output:

▶ best-explain-analyze.py songsearch dummy.sql
EXECUTION TIME
    BEST    1.229ms
    MEAN    1.489ms
    MEDIAN  1.409ms
PLANNING TIME
    BEST    1.994ms
    MEAN    4.557ms
    MEDIAN  2.292ms

The "BEST" is an important metric. More important than mean or median.

Raymond Hettinger explains it better than I do. His context is for benchmarking Python code but it's equally applicable:

"Use the min() rather than the average of the timings. That is a recommendation from me, from Tim Peters, and from Guido van Rossum. The fastest time represents the best an algorithm can perform when the caches are loaded and the system isn't busy with other tasks. All the timings are noisy -- the fastest time is the least noisy. It is easy to show that the fastest timings are the most reproducible and therefore the most useful when timing two different implementations."