Partial Indexes in PostgreSQL
Overview of Partial Indexes
What are Partial Indexes?
With PostgreSQL, users may create partial indexes, which are types of indexes that exist on a subset of a table, rather than the entire table itself. If used correctly, partial indexes improve performance and reduce costs, all while minimizing the amount of storage space they take up on the disk.
Why do we need them?
Let's demonstrate a case where partial indexes may be useful by contrasting them with a non-partial index. If you have many records in an indexed table, the number of records the index needs to track also grows. If the index grows in size, the disk space needed to store the index itself increases as well. In many tables, different records are not accessed with uniform frequency. A subset of a table's records might not be searched very frequently or not searched at all. Records take up precious space in your index whether they are queried or not, and are updated when a new entry is added to the field.
Partial indexes come into the picture to filter unsearched values and give you, as an engineer, a tool to index only what's important.
You can learn more about partial indexes in PostgreSQL here
Advantages of using Partial Indexes
In cases where we know ahead of time the access pattern to a table and can reduce the size of an index by making it partial:
- Response time for SELECT operations is improved because the database searches through a smaller index.
- On average, response time for UPDATE operations is also improved as the index is not going to get updated in all cases.
- Index is smaller in size and can fit into RAM more easily.
- Less space is required to store the index on disk.
Basic PostgreSQL syntax for using Partial Index
CREATE INDEX
index_name
ON
table_name(column_list)
WHERE
condition;
Example of Non-partial Index vs Partial Index in PostgreSQL
Let's see this in action by creating a table with the following command:
CREATE TABLE "vaccination_data" (
id SERIAL PRIMARY KEY,
country varchar(20),
title varchar(10),
names varchar(20),
vaccinated varchar(3)
);
Here is how a portion of the table might look like after inserting values:
SELECT * FROM vaccination_data;
id | country | title | names | vaccinated
-----+--------------------+-------+-------------+------------
1 | Poland | Mr. | Teagan | No
2 | Ukraine | Ms. | Alden | No
3 | Ukraine | Mr. | Ima | No
4 | Colombia | Mr. | Lawrence | Yes
5 | Turkey | Mrs. | Keegan | No
6 | China | Mrs. | Kylan | No
7 | Netherlands | Dr. | Howard | No
...
289690 | Russian Federation | Mrs. | Ray | Yes
289689 | Austria | Dr. | Lenore | Yes
289688 | Sweden | Dr. | Walker | Yes
289687 | Turkey | Dr. | Emerson | No
289686 | Vietnam | Dr. | Addison | Yes
(289686 rows)
In the following example, suppose we want a list of doctors from India that have taken the vaccine. If we want to use normal index, we can create it on the “vaccinated” column with the following command:
CREATE INDEX vaccinated_idx ON vaccination_data(vaccinated);
CREATE INDEX
Time: 333.891 ms
Now, let's check the performance of querying data of doctors from India that have taken the vaccine with the following command:
EXPLAIN ANALYZE
SELECT
*
FROM
vaccination_data
WHERE
vaccinated = 'Yes' AND country = 'India' AND title = 'Dr.';
QUERY PLAN
---------------------------------------------------------------------------
Bitmap Heap Scan on vaccination_data (cost=758.64..4053.40 rows=699 width=25) (actual time=4.142..16.212 rows=582 loops=1)
Recheck Cond: ((vaccinated)::text = 'Yes'::text)
Filter: (((country)::text = 'India'::text) AND ((title)::text = 'Dr.'::text))
Rows Removed by Filter: 69334
Heap Blocks: exact=1337
-> Bitmap Index Scan on vaccinated_idx (cost=0.00..758.46 rows=69072 width=0) (actual time=3.940
..3.941 rows=69916 loops=1)
Index Cond: ((vaccinated)::text = 'Yes'::text)
Planning Time: 0.188 ms
Execution Time: 16.292 ms
(9 rows)
The EXPLAIN command is used for understanding the performance of a query. You can learn more about usage of EXPLAIN command with ANALYZE option here
Notice that total Execution Time is 16.292ms. Also, let's check the index size with the following command:
SELECT pg_size_pretty(pg_relation_size('vaccinated_idx'));
pg_size_pretty
----------------
1984 kB
(1 row)
Now, suppose we want to accelerate the same query using the partial index. Let's begin by dropping the existing index that we created earlier:
DROP INDEX vaccinated_idx;
DROP INDEX
Time: 7.183 ms
In the following command, we have created an index with a WHERE clause that precisely describes list of doctors from India that have taken the vaccine.
CREATE INDEX
vaccinated_idx
ON
vaccination_data(vaccinated)
WHERE
vaccinated = 'Yes' AND country = 'India' AND title = 'Dr.';
CREATE INDEX
Time: 94.567 ms
Notice that the partial index with the WHERE clause is created in 94.567ms, compared to the 333.891ms taken for the non-partial index on the 'vaccinated' column. Let's check the performance of querying list of doctors from India that have taken the vaccine again, using the following command:
EXPLAIN ANALYZE
SELECT
*
FROM
vaccination_data
WHERE
vaccinated = 'Yes' AND country = 'India' AND title = 'Dr.';
QUERY PLAN
---------------------------------------------------------------------------
Index Scan using vaccinated_idx on vaccination_data (cost=0.15..1455.12 rows=699 width=25) (actual time=0.015..0.704 rows=582 loops=1)
Planning Time: 0.442 ms
Execution Time: 0.880 ms
(3 rows)
Observe that total execution time has dropped significantly and is now only 0.880ms, compared to 16.292ms achieved by using a non-partial index on the 'vaccinated' column. Once again, let's check the index size with the following command:
SELECT pg_size_pretty(pg_relation_size('vaccinated_idx'));
pg_size_pretty
----------------
16 kB
(1 row)
As we can observe, the index size for the partial index takes significantly less space (16kb) compared to the non-partial index that we created earlier on the 'vaccinated' column (1984kb).
Here is a summary from our tests:
| Parameter | Non-partial Index | Partial Index | Ratio of change(%) |
|---|---|---|---|
| Estimated start-up cost | 758.64 arbitrary units | 0.15 arbitrary units | 99.9% reduced cost |
| Estimated total cost | 4053.40 arbitrary units | 1455.12 arbitrary units | 64.1% reduced cost |
| Time to create index | 333.891ms | 94.567ms | 71.6% less time |
| Execution time for query with “WHERE” clause | 16.292ms | 0.880ms | 94.5% less time |
| Size of index | 1984kb | 16kb | 99.1% less space |
(Note: The results will vary, depending on the data that is stored in the database)
We have seen that creating a partial index is a better choice where only a small subset of the values stored in the database are accessed frequently. Now, let's see how we can easily manage partial indexes using Atlas.
Managing Partial Indexes is easy with Atlas
Managing partial indexes and database schemas in PostgreSQL can be confusing and error-prone. Atlas is an open-source project which allows us to manage our database using a simple and easy-to-understand declarative syntax (similar to Terraform). We will now learn how to manage partial indexes using Atlas.
If you are just getting started, install the latest version of Atlas using the guide to setting up Atlas.
Managing Partial Index in Atlas
We will first use the atlas schema inspect command to get an HCL representation of the table which we created earlier by using the Atlas CLI:
atlas schema inspect -u "postgres://postgres:mysecretpassword@localhost:5432/vaccination_data?sslmode=disable" > schema.hcl
table "vaccination_data" {
schema = schema.public
column "id" {
null = false
type = serial
}
column "country" {
null = true
type = character_varying(20)
}
column "title" {
null = true
type = character_varying(10)
}
column "names" {
null = true
type = character_varying(20)
}
column "vaccinated" {
null = true
type = character_varying(3)
}
primary_key {
columns = [column.id]
}
}
schema "public" {
}
Now, lets add the following index definition to the file:
index "vaccinated_idx" {
columns = [column.vaccinated]
where = "(vaccinated::text = 'Yes'::text AND country::text = 'India'::text AND title::text = 'Dr.'::text)"
}
Save and apply the schema changes on the database by using the following command:
atlas schema apply -u "postgres://postgres:mysecretpassword@localhost:5432/vaccination_data?sslmode=disable" -f schema.hcl
Atlas generates the necessary SQL statements to add the new partial index to the database schema. Press Enter while the Apply option is highlighted to apply the changes:
-- Planned Changes:
-- Create index "vaccinated_idx" to table: "vaccination_data"
CREATE INDEX "vaccinated_idx" ON "public"."vaccination_data" ("vaccinated") WHERE (vaccinated::text = 'Yes'::text AND country::text = 'India'::text AND title::text = 'Dr.'::text)
✔ Apply
Abort
To verify that our new index was created, open the database command line tool from previous step and run:
SELECT
indexname,
indexdef
FROM
pg_indexes
WHERE
tablename = 'vaccination_data';
[ RECORD 1 ]
indexname | vaccinated_idx
indexdef | CREATE INDEX vaccinated_idx ON public.vaccination_data USING btree (vaccinated) WHERE (((vaccinated)::text = 'Yes'::text) AND ((country)::text = 'India'::text) AND ((title)::text = 'Dr.'::text))
Amazing! Our new partial index is now created!
Limitation of using Partial Index
Partial indexes are useful in cases where we know ahead of time that a table is most frequently queried with a certain WHERE clause. As applications evolve, access patterns to the database also change. Consequently, we may find ourselves in a situation where our index no longer covers many queries, causing them to become resource consuming and slow.
Conclusion
In this section, we learned about PostgreSQL partial indexes and how we can easily create partial indexes in our database by using Atlas.
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