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We are testing the performance of metadata when stored in JSONB compared to a traditional EAV catalogue (commonly used in biomedical scenarios), using a PostgreSQL 9.4 server.
I get a problem when trying to improve the EAV performance with proper indexing. This is an issue that I have encountered also with other queries on other tables so I fear we are making some fundamental mistake.

To model the EAV I have three tables:

1) data that contains all the Entities (not shown)

2) eav_attribute that contains all the Attributes

CREATE TABLE eav_attribute (
    id serial PRIMARY KEY,
    data_type integer NOT NULL,
    loop integer,
    name text NOT NULL,
    field_type text NOT NULL,
    has_unit boolean,
    created_at timestamp with time zone NOT NULL,
    updated_at timestamp with time zone NOT NULL
);

2) eav_value_text_data that contains all the Values

CREATE TABLE eav_value_text_data (
id serial PRIMARY KEY,
entity integer NOT NULL,
attribute integer NOT NULL,
value text NOT NULL,
created_at timestamp with time zone NOT NULL,
updated_at timestamp with time zone NOT NULL
);

With the following foreign key constraint that associates the last two tables (other foreign keys not shown as they are not employed in the query I tried):

ALTER TABLE ONLY eav_value_text_data
ADD CONSTRAINT eav_value_text_data_attribute_fkey
  FOREIGN KEY (attribute) REFERENCES eav_attribute(id);

I have populated the two tables with a huge dataset of genomic variations. I have over 250 million rows on the eav_value_text_data table for 25 million rows in the data table and 49 different attributes (rows in eav_attribute).

Now I am checking the cost and performance of the following query:

SELECT count(*) FROM eav_value_text_data v 
INNER JOIN eav_attribute a ON a.id = v.attribute 
WHERE a.data_type = 11 
AND a.name = 'id' 
AND v.value = 'rs145368920';

The value 'rs145368920' appears only 15 times in eav_value_text_data

First I ran EXPLAIN ANALYZE without adding any index to the two tables:

QUERY PLAN

Aggregate  (cost=5109969.74..5109969.75 rows=1 width=0) (actual time=351086.638..351086.639 rows=1 loops=1)
   ->  Nested Loop  (cost=0.00..5109969.32 rows=168 width=0) (actual time=34814.603..351086.541 rows=15 loops=1)
         Join Filter: (a.attribute = b.id)
         ->  Seq Scan on eav_attribute b  (cost=0.00..1.73 rows=1 width=4) (actual time=0.027..0.034 rows=1 loops=1)
               Filter: ((data_type = 11) AND (name = 'id'::text))
               Rows Removed by Filter: 48
         ->  Seq Scan on eav_value_text_data a  (cost=0.00..5109864.40 rows=8255 width=4) (actual time=34814.555..351086.380 rows=15 loops=1)
               Filter: (value = 'rs145368920'::text)
               Rows Removed by Filter: 252054702
 Planning time: 145.614 ms
 Execution time: 351096.291 ms

It performs to sequential scans on both tables and it requires the quite abysmal time of 350 seconds. Now, I add an index on the column attribute of eav_value_text_data to improve the query performance:

CREATE INDEX eav_value_text_data_attribute_index ON eav_value_text_data (attribute);

And I run again the EXPLAIN ANALYZE:

QUERY PLAN

Aggregate  (cost=2726067.17..2726067.18 rows=1 width=0) (actual time=1262829.858..1262829.859 rows=1 loops=1)
   ->  Nested Loop  (cost=423172.71..2726066.76 rows=166 width=0) (actual time=113152.884..1262829.758 rows=15 loops=1)
         ->  Seq Scan on eav_attribute b  (cost=0.00..1.73 rows=1 width=4) (actual time=0.027..0.062 rows=1 loops=1)
               Filter: ((data_type = 11) AND (name = 'id'::text))
               Rows Removed by Filter: 48
         ->  Bitmap Heap Scan on eav_value_text_data a  (cost=423172.71..2726057.61 rows=741 width=4) (actual time=113152.821..1262829.557 rows=15 loops=1)
               Recheck Cond: (attribute = b.id)
               Rows Removed by Index Recheck: 223460596
               Filter: (value = 'rs145368920'::text)
               Rows Removed by Filter: 24188192
               Heap Blocks: exact=37881 lossy=1921273
               ->  Bitmap Index Scan on eav_value_text_data_attribute_index  (cost=0.00..423172.52 rows=22914127 width=0) (actual time=14177.368..14177.368 rows=24188207 loops=1)
                     Index Cond: (attribute = b.id)
Planning time: 126.846 ms
Execution time: 1262840.302 ms

Even though it is now performing an index scan, it takes now over 1260 seconds which is nearly 4 times worse than without indexes. What is the issue here? Is it related to the high number of lossy heap blocks? Could it be solved just by tuning the work_mem? All the queries were run with cold cache (stopping the Postgres server and flushing the cache) to avoid caching effects.

Currently we have 4GB of RAM on the system where we are running the queries. We have allocated:
work_mem = 4 MB
shared_buffers = 950 MB (about 25% total RAM)
effective_cache_size = 2600 MB (about 70% total RAM)

2

Since the selective predicate on the big table eav_value_text_data big table is v.value = 'rs145368920', you need an index on value more than anything else. The index on attribute is hardly relevant - only in combination with the first to allow index-only scans if possible:

CREATE INDEX eav_value_text_data_val_att_idx ON eav_value_text_data (value, attribute);

This should make a huge difference.
value should be the first column in the index. More:

Index md5 hash as uuid

Since it turns out that your value column is too big to be indexed and you decided to use md5 hashes instead (which is a good solution):

Consider storing the md5 hash as uuid, which would be most efficient (smaller, faster storage). You can just cast the result to uuid:

md5(value)::uuid

Details in the last chapter of this answer:

Related:

Your index would look like this:

CREATE INDEX eav_value_special_idx
ON eav_value_text_data (cast(md5(value) AS uuid), attribute);

Note the explicit cast() syntax in the index definition:

Your query can still use shorthand syntax:

SELECT count(*)
FROM   eav_value_text_data v 
JOIN   eav_attribute a ON a.id = v.attribute 
WHERE  a.data_type = 11 
AND    a.name = 'id' 
AND    md5(v.value)::uuid = md5('rs145368920')::uuid;
  • Thank you so much, Erwin. We had a problem indexing value because the size of some elements exceeded the size limit for BTREE. We indexed the column using the md5 hash of value (as suggested here: postgresql.nabble.com/…) and the query improved a lot. In general which is the best option to test the performance of a cost-expensive query? "Cold cache" or "warm cache"? – user3111884 Mar 21 '15 at 9:50
  • @user3111884: I have some more advice for this. Consider the addendum. The best method to test depends on your typical use case. If the function is called regularly, or if the relevant tables / indices are mostly cached anyway or if most of your DB is typically cached anyway, "warm cache" is the realistic scenario (so this is the common case). Else cold cache is more realistic. – Erwin Brandstetter Mar 21 '15 at 16:24

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