1622615497
Explore the need and usage of hypothetical indexes in PostgreSQL.
In this blog, we will first cover what are hypothetical indexes and what is needed for these types of indexes. Secondly, we shall see the practical usage of these indexes.
As the name suggests, these are not real indexes, these are hypothetical indexes i.e. they are virtual indexes that PostgreSQL query planner does not consider when running queries.
Now the question arises where these **Hypothetical Indexes **are useful?
First, let’s discuss one scenario. We have a large table that is currently in the production environment and we need to make some indexes on live DB, and we are not sure whether that index will be useful or not. We don’t even know if by making that index our production environment may be down!
So, the solution to the above problem will be following:
#database #postgresql #postgres
1626142933
In our last blog, we learned about the Need and Usage of Hypothetical Indexes in PostgreSQL. We can now “check” easily in a live environment, determine if some particular index will be helpful or not, and figure out how we get to know which index to test. To do this, you’ll also need in-depth knowledge of indexing and experience in Postgresql. However, in PostgreSQL, we can get an automatic recommendation of indexes for specific queries by using three extensions hypog, pg_stat_statements, and pg_qualstats.
#postgresql #automatic index recommendations #automatic #index
1598750220
Getting a performance boost with the best usage of indexes, by understanding what’s the data structure, how it works’s/stored, how is it loaded into memory. How Query optimization make’s decision to select indexes.
_Basic understanding of indexes is required i.e what are indexes, index types, creating them. _https://docs.mongodb.com/manual/indexes/
Index on a filed/fields is stored in order that we specify using B-Tree data structure. Stored in ordered Let see what does it mean’s and how it help’s.
snipped from: MognoDB university
snipped from: MognoDB university
snipped from: MognoDB university
Let’s see/visualize how the index are stored on disk. Index stored on disk is managed by the database storage engine itself.
db.getCollection("movieTicket")
.ensureIndex({"showDate":1, "seatNo":1, "status":1});
How the index ({“showDate”:1, “seatNo”:1, “status”:1}) is stored on disk.
showDate_1_seatNo_1_status_1
#index #mongodb #indexing #mongo
1622615497
Explore the need and usage of hypothetical indexes in PostgreSQL.
In this blog, we will first cover what are hypothetical indexes and what is needed for these types of indexes. Secondly, we shall see the practical usage of these indexes.
As the name suggests, these are not real indexes, these are hypothetical indexes i.e. they are virtual indexes that PostgreSQL query planner does not consider when running queries.
Now the question arises where these **Hypothetical Indexes **are useful?
First, let’s discuss one scenario. We have a large table that is currently in the production environment and we need to make some indexes on live DB, and we are not sure whether that index will be useful or not. We don’t even know if by making that index our production environment may be down!
So, the solution to the above problem will be following:
#database #postgresql #postgres
1622615850
HypoPG is a PostgreSQL extension adding support for hypothetical indexes.
An hypothetical – or virtual – index is an index that doesn’t really exists, and thus doesn’t cost CPU, disk or any resource to create. They’re useful to know if specific indexes can increase performance for problematic queries, since you can know if PostgreSQL will use these indexes or not without having to spend resources to create them.
For more thorough informations, please consult the official documentation.
For other general information, you can also consult this blog post.
sudo make install
CREATE EXTENSION hypopg;
Note that hypopg doesn’t provide extension upgrade scripts, as there’s no data saved in any of the objects created. Therefore, you need to first drop the extension then create it again to get the new version.
NOTE: The hypothetical indexes are contained in a single backend. Therefore, if you add multiple hypothetical indexes, concurrent connections doing EXPLAIN
won’t be bothered by your hypothetical indexes.
Assuming a simple test case:
rjuju=# CREATE TABLE hypo AS SELECT id, 'line ' || id AS val FROM generate_series(1,10000) id;
rjuju=# EXPLAIN SELECT * FROM hypo WHERE id = 1;
QUERY PLAN
-------------------------------------------------------
Seq Scan on hypo (cost=0.00..180.00 rows=1 width=13)
Filter: (id = 1)
(2 rows)
The easiest way to create an hypothetical index is to use the hypopg_create_index
functions with a regular CREATE INDEX
statement as arg.
For instance:
rjuju=# SELECT * FROM hypopg_create_index('CREATE INDEX ON hypo (id)');
NOTE: Some information from the CREATE INDEX
statement will be ignored, such as the index name if provided. Some of the ignored information will be handled in a future release.
You can check the available hypothetical indexes in your own backend:
rjuju=# SELECT * FROM hypopg_list_indexes();
indexrelid | indexname | nspname | relname | amname
-----------+-------------------------------------------+---------+---------+--------
205101 | <41072>btree_hypo_id | public | hypo | btree
If you need more technical information on the hypothetical indexes, the hypopg()
function will return the hypothetical indexes in a similar way as pg_index
system catalog.
And now, let’s see if your previous EXPLAIN
statement would use such an index:
rjuju=# EXPLAIN SELECT * FROM hypo WHERE id = 1;
QUERY PLAN
------------------------------------------------------------------------------------
Index Scan using <41072>hypo_btree_hypo_id on hypo (cost=0.29..8.30 rows=1 width=13)
Index Cond: (id = 1)
(2 rows)
Of course, only EXPLAIN
without ANALYZE
will use hypothetical indexes:
rjuju=# EXPLAIN ANALYZE SELECT * FROM hypo WHERE id = 1;
QUERY PLAN
-------------------------------------------------------------------------------------------------
Seq Scan on hypo (cost=0.00..180.00 rows=1 width=13) (actual time=0.036..6.072 rows=1 loops=1)
Filter: (id = 1)
Rows Removed by Filter: 9999
Planning time: 0.109 ms
Execution time: 6.113 ms
(5 rows)
To remove your backend’s hypothetical indexes, you can use the function hypopg_drop_index(indexrelid)
with the OID that the hypopg_list_indexes()
function returns and call hypopg_reset()
to remove all at once, or just close your current connection.
Author: HypoPG
The Demo/Documentation: View The Demo/Documentation
Download Link: Download The Source Code
Official Website: https://github.com/HypoPG/hypopg
#postgresql #database
1597222800
In our previous posts in this series, we spoke at length about using PgBouncer and Pgpool-II , the connection pool architecture and pros and cons of leveraging one for your PostgreSQL deployment. In our final post, we will put them head-to-head in a detailed feature comparison and compare the results of PgBouncer vs. Pgpool-II performance for your PostgreSQL hosting !
The bottom line – Pgpool-II is a great tool if you need load-balancing and high availability. Connection pooling is almost a bonus you get alongside. PgBouncer does only one thing, but does it really well. If the objective is to limit the number of connections and reduce resource consumption, PgBouncer wins hands down.
It is also perfectly fine to use both PgBouncer and Pgpool-II in a chain – you can have a PgBouncer to provide connection pooling, which talks to a Pgpool-II instance that provides high availability and load balancing. This gives you the best of both worlds!
PostgreSQL Connection Pooling: Part 4 – PgBouncer vs. Pgpool-II
While PgBouncer may seem to be the better option in theory, theory can often be misleading. So, we pitted the two connection poolers head-to-head, using the standard pgbench tool, to see which one provides better transactions per second throughput through a benchmark test. For good measure, we ran the same tests without a connection pooler too.
All of the PostgreSQL benchmark tests were run under the following conditions:
We ran each iteration for 5 minutes to ensure any noise averaged out. Here is how the middleware was installed:
Here are the transactions per second (TPS) results for each scenario across a range of number of clients:
#database #developer #performance #postgresql #connection control #connection pooler #connection pooler performance #connection queue #high availability #load balancing #number of connections #performance testing #pgbench #pgbouncer #pgbouncer and pgpool-ii #pgbouncer vs pgpool #pgpool-ii #pooling modes #postgresql connection pooling #postgresql limits #resource consumption #throughput benchmark #transactions per second #without pooling