I have big Postgres database(around 75 GB) and queries are very slow. Is there any way to make them faster?
About database:
List of relations
Schema | Name | Type | Owner | Persistence | Access method | Size | Description
--------+-------------------+----------+----------+-------------+---------------+------------+-------------
public | fingerprints | table | postgres | permanent | heap | 35 GB |
public | songs | table | postgres | permanent | heap | 26 MB |
public | songs_song_id_seq | sequence | postgres | permanent | | 8192 bytes |
\d+ fingerprints
Table "public.fingerprints"
Column | Type | Collation | Nullable | Default | Storage | Compression | Stats target | Description
---------------+-----------------------------+-----------+----------+---------+----------+-------------+--------------+-------------
hash | bytea | | not null | | extended | | |
song_id | integer | | not null | | plain | | |
offset | integer | | not null | | plain | | |
date_created | timestamp without time zone | | not null | now() | plain | | |
date_modified | timestamp without time zone | | not null | now() | plain | | |
Indexes:
"ix_fingerprints_hash" hash (hash)
"uq_fingerprints" UNIQUE CONSTRAINT, btree (song_id, "offset", hash)
Foreign-key constraints:
"fk_fingerprints_song_id" FOREIGN KEY (song_id) REFERENCES songs(song_id) ON DELETE CASCADE
Access method: heap
\d+ songs
Table "public.songs"
Column | Type | Collation | Nullable | Default | Storage | Compression | Stats target | Description
---------------+-----------------------------+-----------+----------+----------------------------------------+----------+-------------+--------------+-------------
song_id | integer | | not null | nextval('songs_song_id_seq'::regclass) | plain | | |
song_name | character varying(250) | | not null | | extended | | |
fingerprinted | smallint | | | 0 | plain | | |
file_sha1 | bytea | | | | extended | | |
total_hashes | integer | | not null | 0 | plain | | |
date_created | timestamp without time zone | | not null | now() | plain | | |
date_modified | timestamp without time zone | | not null | now() | plain | | |
Indexes:
"pk_songs_song_id" PRIMARY KEY, btree (song_id)
Referenced by:
TABLE "fingerprints" CONSTRAINT "fk_fingerprints_song_id" FOREIGN KEY (song_id) REFERENCES songs(song_id) ON DELETE CASCADE
Access method: heap
DB Scheme
DB Amount
No need to write to database, only read. All queries are very simple:
SELECT song_id
WHERE hash in fingerpints = X
EXPLAIN(analyze, buffers, format text) SELECT "song_id", "offset" FROM "fingerprints" WHERE "hash" = decode('eeafdd7ce9130f9697','hex');
QUERY PLAN
-------------------------------------------------------------------------------------------------------------------------------------------
Index Scan using ix_fingerprints_hash on fingerprints (cost=0.00..288.28 rows=256 width=8) (actual time=0.553..234.257 rows=871 loops=1)
Index Cond: (hash = '\xeeafdd7ce9130f9697'::bytea)
Buffers: shared hit=118 read=749
Planning Time: 0.225 ms
Execution Time: 234.463 ms
(5 rows)
234 ms looks fine where it is one query. But in reality there 3000 query per time, that takes about 600 seconds. It is audio recognition application, so algoritm works like that.
About indexes:
CREATE INDEX "ix_fingerprints_hash" ON "fingerprints" USING hash ("hash");
For pooler I use Odyssey.
Little bit of info from config:
shared_buffers = 4GB
huge_pages = try
work_mem = 582kB
maintenance_work_mem = 2GB
effective_io_concurrency = 200
max_worker_processes = 24
max_parallel_workers_per_gather = 12
max_parallel_maintenance_workers = 4
max_parallel_workers = 24
wal_buffers = 16MB
checkpoint_completion_target = 0.9
max_wal_size = 16GB
min_wal_size = 4GB
random_page_cost = 1.1
effective_cache_size = 12GB
Info about hardware:
Xeon 12 core (24 threads)
RAM DDR4 16 GB ECC
NVME disk
Will the database be accelerated by purchase more RAM to handle all DB inside (128 GB in example)? And what parameters should I change to say to Postgres to store db in ram?
I read about several topics about pg_tune, etc. but experiments don't show any good results.
Increasing the RAM so that everything can stay in cache (perhaps after using pg_prewarm to get it into cache in the first place) would certainly work. But it is expensive and shouldn't be necessary.
Having a hash index on something which is already a hashed value is probably not very helpful. Have you tried just a default (btree) index instead?
If you CLUSTER the table on the index over the column named "hash" (which you can only do if it is a btree index) then rows with the same hash code should mostly share the same table page, which would greatly cut down on the number of different buffer reads needed to fetch them all.
If you could get it do a bitmap heap scan instead of an index scan, then it should be able to have a large number of read requests outstanding at a time, due to effective_io_concurrency. But the planner does not account for effective_io_concurrency when doing planning, which means it won't choose a bitmap heap scan specifically to get it that benefit. Normally an index read finding hundreds of rows on different pages would automatically choose a bitmap heap scan method, but in your case it is probably the low setting of random_page_cost which is inhibiting it from doing so. The low setting of random_page_cost is probably reasonable in itself, but it does have this unfortunate side effect. A problem with this strategy is that it doesn't reduce the overall amount of IO needed, it just allows them overlap and so make better use of multiple IO channels. But if many sessions are running many instances of this query at the same time, they will start filling up those channels and so start competing with each other. So the CLUSTER method is probably superior as it gets the same answer with less IO. If you want to play around with bitmap scans, you could temporarily increase random_page_cost or temporarily set enable_indexscan to off.
No need to write to database, only read.
So the DB is read-only.
And in comments:
db worked fine on small amount of data(few GB), but after i filled out database started to slowdown.
So indexes have been built up incrementally.
Indexes
UNIQUE CONSTRAINT on (song_id, "offset", hash)
I would replace that with:
ALTER TABLE fingerprints
DROP CONSTRAINT uq_fingerprints
, ADD CONSTRAINT uq_fingerprints UNIQUE(hash, song_id, "offset") WITH (FILLFACTOR = 100)
This enforces the same constraint, but the leading hash column in the underlying B-tree index now supports the filter on hash in your displayed query. And the fact that all needed columns are included in the index, further allows much faster index-only scans. The (smaller) index should also be more easily cached than the (bigger) table (plus index).
See:
Is a composite index also good for queries on the first field?
Also rewrites the index in pristine condition, and with FILLFACTOR 100 for the read-only DB. (Instead of the default 90 for a B-tree index.)
Hash index on (hash) and CLUSTER
The name of the column "hash" has nothing to do with the name of the index type, which also happens to be "hash". (The column should probably not be named "hash" to begin with.)
If (and only if) you also have other queries centered around one of few hash values, that cannot use index-only scans (and you actually see faster queries than without) keep the hash index, additionally. And optimize it. (Else drop it!)
ALTER INDEX ix_fingerprints_hash SET (FILLFACTOR = 100);
An incrementally grown index may end up with bloat or unbalanced overflow pages in case of a hash index. REINDEX should take care of that. While being at it, increase FILLFACTER to 100 (from the default 75 for a hash index) for your read-only (!) DB. You can REINDEX to make the change effective.
REINDEX INDEX ix_fingerprints_hash;
Or you can CLUSTER (like jjanes already suggested) on the rearranged B-tree index from above:
CLUSTER fingerprints USING uq_fingerprints;
Rewrites the table and all indexes; rows are physically sorted according to the given index, so "clustered" around the leading column(s). Effects are permanent for your read-only DB. But index-only scans do not benefit from this.
When done optimizing, run once:
VACUUM ANALYZE fingerprints;
work_mem
The tiny setting for work_mem stands out:
work_mem = 582kB
Even the (very conservative!) default is 4MB.
But after reading your question again, it would seem you only have tiny queries. So maybe that's ok after all.
Else, with 16GB RAM you can typically afford a 100 times as much. Depends on your work load of course.
Many small queries, many parallel workers --> keep small work_mem (like 4MB?)
Few big queries, few parallel workers --> go high (like 256MB? or more)
Large amounts of temporary files written in your database over time, and mentions of "disk" in the output of EXPLAIN ANALYZE would indicate the need for more work_mem.
Additional questions
Will the database be accelerated by purchase more RAM to handle all DB inside (128 GB in example)?
More RAM almost always helps until the whole DB can be cached in RAM and all processes can afford all the work_mem they desire.
And what parameters should I change to say to Postgres to store db in ram?
Everything that's read from the database is cached automatically in system cache and Postgres cache, up to the limit of available RAM. (Setting work_mem too high competes for that same resource.)
Index bloats are reaching 57%, while table bloat is 9% only and autovacuum_vacuum_Scale_factor is 10% only.
what is more surprising is even primary key is having bloat of 57%. My understanding is since my primary key is auto incrementing and single column key only so after 10% of table dead tuples, primary key index should also have 10% dead tuples.
Now when autovacuum will run at 10% of dead tuples , it will clean dead tuples. The dead tuple space now becomes bloat and this should be reused by new updates, insert. But this isn't happening in my database, here bloat size keeps on increasing.
FYI:
Index Bloat:
current_database | schemaname | tblname | idxname | real_size | extra_size | extra_ratio | fillfactor | bloat_size | bloat_ratio
| is_na
------------------+------------+----------------------+----------------------------------------------------------+------------+------------+------------------+------------+------------+-------------------
+-------
stackdb | public | data_entity | data_entity_pkey | 2766848000 | 1704222720 | 61.5943745373797 | 90 | 1585192960 | 57.2923760177646
Table Bloat:
current_database | schemaname | tblname | real_size | extra_size | extra_ratio | fillfactor | bloat_size | bloat_ratio | is_na
stackdb | public | data_entity | 10106732544 | 1007288320 | 9.96650812332014 | 100 | 1007288320 | 9.96650812332014 | f
Autovacuum Settings:
stackdb=> show autovacuum_vacuum_scale_factor;
autovacuum_vacuum_scale_factor
--------------------------------
0.1
(1 row)
stackdb=> show autovacuum_vacuum_threshold;
autovacuum_vacuum_threshold
-----------------------------
50
(1 row)
Note:
autovacuum is on
autovacuum is running successfully at defined intervals.
postgreSQL is running version 10.6. Same issue has been found with version 12.x
First: an index bloat of 57% is totally healthy. Don't worry.
Indexes become more bloated than tables, because the empty space cannot be reused as freely as it can be in a table. The table, also known al “heap”, has no predetermined ordering: if a new row is written as the result of an INSERT or UPDATE, it ends up in the first page that has enough free space, so it is easy to keep bloat low if VACUUM does its job.
B-tree indexes are different: their entries have a certain ordering, so the database is not free to choose where to put the new row. So you may have to put it into a page that is already full, causing a page split, while elsewhere in the index there are pages that are almost empty.
I have a db that contains username with 3 different phone numbers and 3 different ids. also we will have 3 different type of notes for each username.
I am using postgres and data are planned to increase to millions of rows. querying and inserting new data process are really important to be fastest way.
which schema would be better for that:
username | no1(string) | no2(string) | no3(string) | id1(string) | id2(string) | | id3(string) | note1(string) | note2(string) | note3(string)
OR
username | no(JSON) | id(JSON) | note(JSON)
I have a quite large table in the database. Size of table and its indexes are shown in tables below:
table_size
----------------
22 GB
schemaname | scan_count | tablename | indexname | index_size
------------+------------+-----------+-----------------------------------------------------------------+------------
public | 1352665306 | t1 | ind1 | 6686 MB
public | 1492127808 | t1 | ind2 | 6587 MB
public | 3492322 | t1 | ind3 | 4747 MB
public | 71810172 | t1 | ind4 | 4237 MB
public | 80547954 | t1 | cluster_ind | 4035 MB
public | 3628773 | t1 | ind6 | 3700 MB
As you can see, tables size is 22GB. It has 6 different indexes, which take 30GB of space combined.
According to postgresql docs:
CLUSTER can re-sort the table using either an index scan on the specified index, or (if the index is a b-tree) a sequential scan followed by sorting. It will attempt to choose the method that will be faster, based on planner cost parameters and available statistical information.
When an index scan is used, a temporary copy of the table is created that contains the table data in the index order. Temporary copies of each index on the table are created as well. Therefore, you need free space on disk at least equal to the sum of the table size and the index sizes.
However, with df -h showing that I have 75GB of free space on the partition containing my postgresql data, I still run out of disk space when I try to cluster the table over cluster_ind index using the command below:
cluster table using cluster_ind;
and I face this error:
ERROR: could not extend file "base/16385/2165933.3": wrote only 4096 of 8192 bytes at block 394731
HINT: Check free disk space.
Question: What else is using disk space during clustering other than table size and sum of sizes of its indexes? And how can I estimate this space required to run a CLUSTER command on a table using an index?
That could be the TOAST table.
Use pg_total_relation_size('table_name') to get the full size of the table.
Our team is having a lot of issues with the Spark API particularly with large schema tables. We currently have a program written in Scala that utilizes the Apache spark API to create two Hive tables from raw files. We have one particularly very large raw data file that is giving us issues that contains around ~4700 columns and ~200,000 rows.
Every week we get a new file that shows the updates, inserts and deletes that happened in the last week. Our program will create two tables – a master table and a history table. The master table will be the most up to date version of this table while the history table shows all changes inserts and updates that happened to this table and showing what changed. For example, if we have the following schema where A and B are the primary keys:
Week 1 Week 2
|-----|-----|-----| |-----|-----|-----|
| A | B | C | | A | B | C |
|-----|-----|-----| |-----|-----|-----|
| 1 | 2 | 3 | | 1 | 2 | 4 |
|-----|-----|-----| |-----|-----|-----|
Then the master table will now be
|-----|-----|-----|
| A | B | C |
|-----|-----|-----|
| 1 | 2 | 4 |
|-----|-----|-----|
And The history table will be
|-----|-----|-------------------|----------------|-------------|-------------|
| A | B | changed_column | change_type | old_value | new_value |
|-----|-----|-------------------|----------------|-------------|-------------|
| 1 | 2 | C | Update | 3 | 4 |
|-----|-----|-------------------|----------------|-------------|-------------|
This process is working flawlessly for shorter schema tables. We have a table that has 300 columns but over 100,000,000 rows and this code still runs as expected. The process above for the larger schema table runs for around 15 hours, and then crashes with the following error:
Exception in thread "main" java.lang.StackOverflowError
at scala.collection.generic.Growable$class.loop$1(Growable.scala:52)
at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:57)
at scala.collection.mutable.ListBuffer.$plus$plus$eq(ListBuffer.scala:183)
at scala.collection.mutable.ListBuffer.$plus$plus$eq(ListBuffer.scala:45)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.immutable.List.foreach(List.scala:381)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
at scala.collection.immutable.List.flatMap(List.scala:344)
Here is a code example that takes around 4 hours to run for this larger table, but runs in 20 seconds for other tables:
var dataframe_result = dataframe1.join(broadcast(dataframe2), Seq(listOfUniqueIds:_*)).repartition(100).cache()
We have tried all of the following with no success:
Using hash broad-cast joins (dataframe2 is smaller, dataframe1 is huge)
Repartioining on different numbers, as well as not repartitioning at all
Caching the result of the dataframe (we originally did not do this).
What is causing this error and how can we fix it? The only difference between this problem table is that it has so many columns. Is there an upper limit to how many columns Spark can handle?
Note: We are running this code on a very large MAPR cluster and we tried giving the code 500GB of RAM and its still failing.