We work for some of the largest PostgreSQL database infrastructure globally, Often we get query on how to configure PostgreSQL for performance, scalability and reliability so thought will write a post on configuring PostgreSQL for performance. PostgreSQL is a highly tunable database system but setting wrong values for PostgreSQL configuration parameters will negatively impact your overall PostgreSQL performance. Setting too high values won’t be always good for PostgreSQL performance so we strongly recommend to understand the sizing of configuration parameters before changing their default values. Please don’t consider this post a generic PostgreSQL configuration run-book or recommendations for your production infrastructure, You can definitely use this as a checklist for configuring your PostgreSQL infrastructure, This post covers tuning PostgreSQL configuration parameters like max_connections, shared_buffers, effective_cache_size, work_mem, maintenance_work_mem, seq_page_cost, random_page_cost, wal_buffers, autovacuum etc. Below we have explained how this post / presentation is organized:

• Configuring PostgreSQL connection handling for performance
• How to tune PostgreSQL memory parameters for performance and scalability ?
• Tuning PostgreSQL optimizer for performance and efficient indexing
• Configuring PostgreSQL WAL files for performance
• Tuning PostgreSQL writer process
• Tuning PostgreSQL checkpointing
• Tuning PostgreSQL autovacuum
• Troubleshooting PostgreSQL performance with logs

Download our featured whitepaper on configuring PostgreSQL for performance. If you’re a PostgreSQL DBA or an technology executive looking to improve performance and prepare for high traffic PostgreSQL infrastructure, this is a must-read!

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The post How do we configure PostgreSQL for performance ? appeared first on The WebScale Database Infrastructure Operations Experts.

We often get this question, What are the most important things a PostgreSQL DBA should do to guarantee optimal performance and reliability, Do we have checklist for PostgreSQL DBAs to follow daily ? Since we are getting this question too often, Thought let’s note it as blog post and share with community of PostgreSQL ecosystem. The only objective this post is to share the information, Please don’t consider this as a run-book or recommendation from MinervaDB PostgreSQL support. We at MinervaDB are not accountable of any negative performance in you PostgreSQL performance with running these scripts in production database infrastructure of your business, The following is a simple daily checklist for PostgreSQL DBA:

Task 1: Check that all the PostgreSQL instances are up and operational:

pgrep -u postgres -fa -- -D

What if you have several instances of PostgreSQL are running:

pgrep -fa -- -D |grep postgres

Task 2: Monitoring PostgreSQL logsRecord PostgreSQL error logs: Open postgresql.conf configuration file, Under the ERROR REPORTING AND LOGGING section of the file, use following config parameters:

log_destination = 'stderr'
logging_collector = on
log_directory = 'pg_log'
log_filename = 'postgresql-%Y-%m-%d_%H%M%S.log'
log_truncate_on_rotation = off
log_rotation_age = 1d
log_min_duration_statement = 0
log_connections = on
log_duration = on
log_hostname = on
log_timezone = 'UTC'

Save the postgresql.conf file and restart the postgres server.

sudo service postgresql restart

Task 3: Confirm PostgreSQL backup completed successfully 

Use backup logs (possible only with PostgreSQL logical backup) to audit backup quality:

$ pg_dumpall > /backup-path/pg-backup-dump.sql > /var/log/postgres/pg-backup-dump.log

Task 4: Monitoring PostgreSQL Database Size:

select datname, pg_size_pretty(pg_database_size(datname)) from pg_database order by pg_database_size(datname);

Task 5: Monitor all PostgreSQL queries running (please repeat this task every 90 minutes during business / peak hours):

SELECT pid, age(clock_timestamp(), query_start), usename, query 
FROM pg_stat_activity 
WHERE query != '<IDLE>' AND query NOT ILIKE '%pg_stat_activity%' 
ORDER BY query_start desc;

Task 6: Inventory of indexes in PostgreSQL database:

select
    t.relname as table_name,
    i.relname as index_name,
    string_agg(a.attname, ',') as column_name
from
    pg_class t,
    pg_class i,
    pg_index ix,
    pg_attribute a
where
    t.oid = ix.indrelid
    and i.oid = ix.indexrelid
    and a.attrelid = t.oid
    and a.attnum = ANY(ix.indkey)
    and t.relkind = 'r'
    and t.relname not like 'pg_%'
group by  
    t.relname,
    i.relname
order by
    t.relname,
    i.relname;

Task 7: Finding the largest databases in your PostgreSQL cluster

SELECT d.datname as Name,  pg_catalog.pg_get_userbyid(d.datdba) as Owner,
    CASE WHEN pg_catalog.has_database_privilege(d.datname, 'CONNECT')
        THEN pg_catalog.pg_size_pretty(pg_catalog.pg_database_size(d.datname))
        ELSE 'No Access'
    END as Size
FROM pg_catalog.pg_database d
    order by
    CASE WHEN pg_catalog.has_database_privilege(d.datname, 'CONNECT')
        THEN pg_catalog.pg_database_size(d.datname)
        ELSE NULL
    END desc -- nulls first
    LIMIT 20

Task 8: when you are suspecting some serious performance bottleneck in PostgreSQL ? Especially when you suspecting transactions blocking each other:

WITH RECURSIVE l AS (
  SELECT pid, locktype, mode, granted,
ROW(locktype,database,relation,page,tuple,virtualxid,transactionid,classid,objid,objsubid) obj
  FROM pg_locks
), pairs AS (
  SELECT w.pid waiter, l.pid locker, l.obj, l.mode
  FROM l w
  JOIN l ON l.obj IS NOT DISTINCT FROM w.obj AND l.locktype=w.locktype AND NOT l.pid=w.pid AND l.granted
  WHERE NOT w.granted
), tree AS (
  SELECT l.locker pid, l.locker root, NULL::record obj, NULL AS mode, 0 lvl, locker::text path, array_agg(l.locker) OVER () all_pids
  FROM ( SELECT DISTINCT locker FROM pairs l WHERE NOT EXISTS (SELECT 1 FROM pairs WHERE waiter=l.locker) ) l
  UNION ALL
  SELECT w.waiter pid, tree.root, w.obj, w.mode, tree.lvl+1, tree.path||'.'||w.waiter, all_pids || array_agg(w.waiter) OVER ()
  FROM tree JOIN pairs w ON tree.pid=w.locker AND NOT w.waiter = ANY ( all_pids )
)
SELECT (clock_timestamp() - a.xact_start)::interval(3) AS ts_age,
       replace(a.state, 'idle in transaction', 'idletx') state,
       (clock_timestamp() - state_change)::interval(3) AS change_age,
       a.datname,tree.pid,a.usename,a.client_addr,lvl,
       (SELECT count(*) FROM tree p WHERE p.path ~ ('^'||tree.path) AND NOT p.path=tree.path) blocked,
       repeat(' .', lvl)||' '||left(regexp_replace(query, 's+', ' ', 'g'),100) query
FROM tree
JOIN pg_stat_activity a USING (pid)
ORDER BY path;

Task 9:  Identify bloated Tables in PostgreSQL :

WITH constants AS (
    -- define some constants for sizes of things
    -- for reference down the query and easy maintenance
    SELECT current_setting('block_size')::numeric AS bs, 23 AS hdr, 8 AS ma
),
no_stats AS (
    -- screen out table who have attributes
    -- which dont have stats, such as JSON
    SELECT table_schema, table_name, 
        n_live_tup::numeric as est_rows,
        pg_table_size(relid)::numeric as table_size
    FROM information_schema.columns
        JOIN pg_stat_user_tables as psut
           ON table_schema = psut.schemaname
           AND table_name = psut.relname
        LEFT OUTER JOIN pg_stats
        ON table_schema = pg_stats.schemaname
            AND table_name = pg_stats.tablename
            AND column_name = attname 
    WHERE attname IS NULL
        AND table_schema NOT IN ('pg_catalog', 'information_schema')
    GROUP BY table_schema, table_name, relid, n_live_tup
),
null_headers AS (
    -- calculate null header sizes
    -- omitting tables which dont have complete stats
    -- and attributes which aren't visible
    SELECT
        hdr+1+(sum(case when null_frac <> 0 THEN 1 else 0 END)/8) as nullhdr,
        SUM((1-null_frac)*avg_width) as datawidth,
        MAX(null_frac) as maxfracsum,
        schemaname,
        tablename,
        hdr, ma, bs
    FROM pg_stats CROSS JOIN constants
        LEFT OUTER JOIN no_stats
            ON schemaname = no_stats.table_schema
            AND tablename = no_stats.table_name
    WHERE schemaname NOT IN ('pg_catalog', 'information_schema')
        AND no_stats.table_name IS NULL
        AND EXISTS ( SELECT 1
            FROM information_schema.columns
                WHERE schemaname = columns.table_schema
                    AND tablename = columns.table_name )
    GROUP BY schemaname, tablename, hdr, ma, bs
),
data_headers AS (
    -- estimate header and row size
    SELECT
        ma, bs, hdr, schemaname, tablename,
        (datawidth+(hdr+ma-(case when hdr%ma=0 THEN ma ELSE hdr%ma END)))::numeric AS datahdr,
        (maxfracsum*(nullhdr+ma-(case when nullhdr%ma=0 THEN ma ELSE nullhdr%ma END))) AS nullhdr2
    FROM null_headers
),
table_estimates AS (
    -- make estimates of how large the table should be
    -- based on row and page size
    SELECT schemaname, tablename, bs,
        reltuples::numeric as est_rows, relpages * bs as table_bytes,
    CEIL((reltuples*
            (datahdr + nullhdr2 + 4 + ma -
                (CASE WHEN datahdr%ma=0
                    THEN ma ELSE datahdr%ma END)
                )/(bs-20))) * bs AS expected_bytes,
        reltoastrelid
    FROM data_headers
        JOIN pg_class ON tablename = relname
        JOIN pg_namespace ON relnamespace = pg_namespace.oid
            AND schemaname = nspname
    WHERE pg_class.relkind = 'r'
),
estimates_with_toast AS (
    -- add in estimated TOAST table sizes
    -- estimate based on 4 toast tuples per page because we dont have 
    -- anything better.  also append the no_data tables
    SELECT schemaname, tablename, 
        TRUE as can_estimate,
        est_rows,
        table_bytes + ( coalesce(toast.relpages, 0) * bs ) as table_bytes,
        expected_bytes + ( ceil( coalesce(toast.reltuples, 0) / 4 ) * bs ) as expected_bytes
    FROM table_estimates LEFT OUTER JOIN pg_class as toast
        ON table_estimates.reltoastrelid = toast.oid
            AND toast.relkind = 't'
),
table_estimates_plus AS (
-- add some extra metadata to the table data
-- and calculations to be reused
-- including whether we cant estimate it
-- or whether we think it might be compressed
    SELECT current_database() as databasename,
            schemaname, tablename, can_estimate, 
            est_rows,
            CASE WHEN table_bytes > 0
                THEN table_bytes::NUMERIC
                ELSE NULL::NUMERIC END
                AS table_bytes,
            CASE WHEN expected_bytes > 0 
                THEN expected_bytes::NUMERIC
                ELSE NULL::NUMERIC END
                    AS expected_bytes,
            CASE WHEN expected_bytes > 0 AND table_bytes > 0
                AND expected_bytes <= table_bytes
                THEN (table_bytes - expected_bytes)::NUMERIC
                ELSE 0::NUMERIC END AS bloat_bytes
    FROM estimates_with_toast
    UNION ALL
    SELECT current_database() as databasename, 
        table_schema, table_name, FALSE, 
        est_rows, table_size,
        NULL::NUMERIC, NULL::NUMERIC
    FROM no_stats
),
bloat_data AS (
    -- do final math calculations and formatting
    select current_database() as databasename,
        schemaname, tablename, can_estimate, 
        table_bytes, round(table_bytes/(1024^2)::NUMERIC,3) as table_mb,
        expected_bytes, round(expected_bytes/(1024^2)::NUMERIC,3) as expected_mb,
        round(bloat_bytes*100/table_bytes) as pct_bloat,
        round(bloat_bytes/(1024::NUMERIC^2),2) as mb_bloat,
        table_bytes, expected_bytes, est_rows
    FROM table_estimates_plus
)
-- filter output for bloated tables
SELECT databasename, schemaname, tablename,
    can_estimate,
    est_rows,
    pct_bloat, mb_bloat,
    table_mb
FROM bloat_data
-- this where clause defines which tables actually appear
-- in the bloat chart
-- example below filters for tables which are either 50%
-- bloated and more than 20mb in size, or more than 25%
-- bloated and more than 4GB in size
WHERE ( pct_bloat >= 50 AND mb_bloat >= 10 )
    OR ( pct_bloat >= 25 AND mb_bloat >= 1000 )
ORDER BY pct_bloat DESC;

Task 10:  Identify bloated indexes in PostgreSQL :

-- btree index stats query
-- estimates bloat for btree indexes
WITH btree_index_atts AS (
    SELECT nspname, 
        indexclass.relname as index_name, 
        indexclass.reltuples, 
        indexclass.relpages, 
        indrelid, indexrelid,
        indexclass.relam,
        tableclass.relname as tablename,
        regexp_split_to_table(indkey::text, ' ')::smallint AS attnum,
        indexrelid as index_oid
    FROM pg_index
    JOIN pg_class AS indexclass ON pg_index.indexrelid = indexclass.oid
    JOIN pg_class AS tableclass ON pg_index.indrelid = tableclass.oid
    JOIN pg_namespace ON pg_namespace.oid = indexclass.relnamespace
    JOIN pg_am ON indexclass.relam = pg_am.oid
    WHERE pg_am.amname = 'btree' and indexclass.relpages > 0
         AND nspname NOT IN ('pg_catalog','information_schema')
    ),
index_item_sizes AS (
    SELECT
    ind_atts.nspname, ind_atts.index_name, 
    ind_atts.reltuples, ind_atts.relpages, ind_atts.relam,
    indrelid AS table_oid, index_oid,
    current_setting('block_size')::numeric AS bs,
    8 AS maxalign,
    24 AS pagehdr,
    CASE WHEN max(coalesce(pg_stats.null_frac,0)) = 0
        THEN 2
        ELSE 6
    END AS index_tuple_hdr,
    sum( (1-coalesce(pg_stats.null_frac, 0)) * coalesce(pg_stats.avg_width, 1024) ) AS nulldatawidth
    FROM pg_attribute
    JOIN btree_index_atts AS ind_atts ON pg_attribute.attrelid = ind_atts.indexrelid AND pg_attribute.attnum = ind_atts.attnum
    JOIN pg_stats ON pg_stats.schemaname = ind_atts.nspname
          -- stats for regular index columns
          AND ( (pg_stats.tablename = ind_atts.tablename AND pg_stats.attname = pg_catalog.pg_get_indexdef(pg_attribute.attrelid, pg_attribute.attnum, TRUE)) 
          -- stats for functional indexes
          OR   (pg_stats.tablename = ind_atts.index_name AND pg_stats.attname = pg_attribute.attname))
    WHERE pg_attribute.attnum > 0
    GROUP BY 1, 2, 3, 4, 5, 6, 7, 8, 9
),
index_aligned_est AS (
    SELECT maxalign, bs, nspname, index_name, reltuples,
        relpages, relam, table_oid, index_oid,
        coalesce (
            ceil (
                reltuples * ( 6 
                    + maxalign 
                    - CASE
                        WHEN index_tuple_hdr%maxalign = 0 THEN maxalign
                        ELSE index_tuple_hdr%maxalign
                      END
                    + nulldatawidth 
                    + maxalign 
                    - CASE /* Add padding to the data to align on MAXALIGN */
                        WHEN nulldatawidth::integer%maxalign = 0 THEN maxalign
                        ELSE nulldatawidth::integer%maxalign
                      END
                )::numeric 
              / ( bs - pagehdr::NUMERIC )
              +1 )
         , 0 )
      as expected
    FROM index_item_sizes
),
raw_bloat AS (
    SELECT current_database() as dbname, nspname, pg_class.relname AS table_name, index_name,
        bs*(index_aligned_est.relpages)::bigint AS totalbytes, expected,
        CASE
            WHEN index_aligned_est.relpages <= expected 
                THEN 0
                ELSE bs*(index_aligned_est.relpages-expected)::bigint 
            END AS wastedbytes,
        CASE
            WHEN index_aligned_est.relpages <= expected
                THEN 0 
                ELSE bs*(index_aligned_est.relpages-expected)::bigint * 100 / (bs*(index_aligned_est.relpages)::bigint) 
            END AS realbloat,
        pg_relation_size(index_aligned_est.table_oid) as table_bytes,
        stat.idx_scan as index_scans
    FROM index_aligned_est
    JOIN pg_class ON pg_class.oid=index_aligned_est.table_oid
    JOIN pg_stat_user_indexes AS stat ON index_aligned_est.index_oid = stat.indexrelid
),
format_bloat AS (
SELECT dbname as database_name, nspname as schema_name, table_name, index_name,
        round(realbloat) as bloat_pct, round(wastedbytes/(1024^2)::NUMERIC) as bloat_mb,
        round(totalbytes/(1024^2)::NUMERIC,3) as index_mb,
        round(table_bytes/(1024^2)::NUMERIC,3) as table_mb,
        index_scans
FROM raw_bloat
)
-- final query outputting the bloated indexes
-- change the where and order by to change
-- what shows up as bloated
SELECT *
FROM format_bloat
WHERE ( bloat_pct > 50 and bloat_mb > 10 )
ORDER BY bloat_mb DESC;

Task 11:  Monitor blocked and blocking activities in PostgreSQL:

 SELECT blocked_locks.pid     AS blocked_pid,
         blocked_activity.usename  AS blocked_user,
         blocking_locks.pid     AS blocking_pid,
         blocking_activity.usename AS blocking_user,
         blocked_activity.query    AS blocked_statement,
         blocking_activity.query   AS current_statement_in_blocking_process
   FROM  pg_catalog.pg_locks         blocked_locks
    JOIN pg_catalog.pg_stat_activity blocked_activity  ON blocked_activity.pid = blocked_locks.pid
    JOIN pg_catalog.pg_locks         blocking_locks 
        ON blocking_locks.locktype = blocked_locks.locktype
        AND blocking_locks.database IS NOT DISTINCT FROM blocked_locks.database
        AND blocking_locks.relation IS NOT DISTINCT FROM blocked_locks.relation
        AND blocking_locks.page IS NOT DISTINCT FROM blocked_locks.page
        AND blocking_locks.tuple IS NOT DISTINCT FROM blocked_locks.tuple
        AND blocking_locks.virtualxid IS NOT DISTINCT FROM blocked_locks.virtualxid
        AND blocking_locks.transactionid IS NOT DISTINCT FROM blocked_locks.transactionid
        AND blocking_locks.classid IS NOT DISTINCT FROM blocked_locks.classid
        AND blocking_locks.objid IS NOT DISTINCT FROM blocked_locks.objid
        AND blocking_locks.objsubid IS NOT DISTINCT FROM blocked_locks.objsubid
        AND blocking_locks.pid != blocked_locks.pid

    JOIN pg_catalog.pg_stat_activity blocking_activity ON blocking_activity.pid = blocking_locks.pid
   WHERE NOT blocked_locks.granted;

Task 12: Monitoring PostgreSQL Disk I/O performance

-- perform a "select pg_stat_reset();" when you want to reset counter statistics
with 
all_tables as
(
SELECT  *
FROM    (
    SELECT  'all'::text as table_name, 
        sum( (coalesce(heap_blks_read,0) + coalesce(idx_blks_read,0) + coalesce(toast_blks_read,0) + coalesce(tidx_blks_read,0)) ) as from_disk, 
        sum( (coalesce(heap_blks_hit,0)  + coalesce(idx_blks_hit,0)  + coalesce(toast_blks_hit,0)  + coalesce(tidx_blks_hit,0))  ) as from_cache    
    FROM    pg_statio_all_tables  --> change to pg_statio_USER_tables if you want to check only user tables (excluding postgres's own tables)
    ) a
WHERE   (from_disk + from_cache) > 0 -- discard tables without hits
),
tables as 
(
SELECT  *
FROM    (
    SELECT  relname as table_name, 
        ( (coalesce(heap_blks_read,0) + coalesce(idx_blks_read,0) + coalesce(toast_blks_read,0) + coalesce(tidx_blks_read,0)) ) as from_disk, 
        ( (coalesce(heap_blks_hit,0)  + coalesce(idx_blks_hit,0)  + coalesce(toast_blks_hit,0)  + coalesce(tidx_blks_hit,0))  ) as from_cache    
    FROM    pg_statio_all_tables --> change to pg_statio_USER_tables if you want to check only user tables (excluding postgres's own tables)
    ) a
WHERE   (from_disk + from_cache) > 0 -- discard tables without hits
)
SELECT  table_name as "table name",
    from_disk as "disk hits",
    round((from_disk::numeric / (from_disk + from_cache)::numeric)*100.0,2) as "% disk hits",
    round((from_cache::numeric / (from_disk + from_cache)::numeric)*100.0,2) as "% cache hits",
    (from_disk + from_cache) as "total hits"
FROM    (SELECT * FROM all_tables UNION ALL SELECT * FROM tables) a
ORDER   BY (case when table_name = 'all' then 0 else 1 end), from_disk desc

References 

• https://www.postgresql.org/developer/related-projects/ 
• https://www.postgresql.org/community/
• https://github.com/pgexperts

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The post PostgreSQL DBA Daily Checklist appeared first on The WebScale Database Infrastructure Operations Experts.

Introduction  

If you are doing a very detailed Performance Diagnostics / Forensics then we strongly recommend you to understand the Data Access Path of underlying queries, cost of query execution, wait events / locks and system resource usage by PostgreSQL infrastructure operations. MinervaDB Performance Engineering Team measures performance by “Response Time” , So finding slow queries in PostgreSQL will be the most appropriate point to start this blog. PostgreSQL Server is highly configurable to collect details on query performance: slow query log, auditing execution plans with auto_explain and querying pg_stat_statements  . 

Using PostgreSQL slow query log to troubleshoot the performance

Step 1 – Open postgresql.conf file in your favorite text editor ( In Ubuntu, postgreaql.conf is available on /etc/postgresql/ ) and update configuration parameter log_min_duration_statement , By default configuration the slow query log is not active, To enable the slow query log on globally, you can change postgresql.conf:

log_min_duration_statement = 2000

In the above configuration, PostgreSQL will log queries, which take longer than 2 seconds.

Step 2 – A “reload” (by simply calling the SQL function) is sufficient, there is no need for a PostgreSQL server restart and Don’t worry, it won’t interrupt any active connections:

postgres=# SELECT pg_reload_conf();
 pg_reload_conf 
----------------
 t 
(1 row)

Note: It’s often too heavy for PostgreSQL infrastructure if you change slow query log settings in postgresql.conf , Therefore it makes more sensible to change only for a selected database or user:

postgres=# ALTER DATABASE minervadb SET log_min_duration_statement = 2000;
ALTER DATABASE

To complete the detailed performance forensics / diagnostics of high latency queries you can use aut0_explain , We have explained same below for queries exceeding certain threshold in PostgreSQL to send plan to the log file:

postgres=# LOAD 'auto_explain';
LOAD
postgres=# SET auto_explain.log_analyze TO on;
SET
postgres=# SET auto_explain.log_min_duration TO 2000;
SET

You can also enable auto explain in postgresql.conf with the settings below:

session_preload_libraries = 'auto_explain';

Note: Please do not forget to call pg_reload_conf() after the change made to postgresql.conf

More examples on PostgreSQL auto explain is copied below:

postgres=# CREATE TABLE minervdb_bench  AS
postgres-# SELECT * FROM generate_series(1, 10000000) AS id;
SELECT 10000000
postgres=# CREATE INDEX idx_id ON minervadb_bench(id);

postgres=# CREATE INDEX idx_id ON minervdb_bench(id);
CREATE INDEX
postgres=# ANALYZE;
ANALYZE

postgres=# LOAD 'auto_explain';
LOAD
postgres=# SET auto_explain.log_analyze TO on;
SET
postgres=# SET auto_explain.log_min_duration TO 200;
SET

postgres=# explain SELECT * FROM minervdb_bench  WHERE id < 5000;
                                      QUERY PLAN                                       
---------------------------------------------------------------------------------------
 Index Only Scan using idx_id on minervdb_bench  (cost=0.43..159.25 rows=4732 width=4)
   Index Cond: (id < 5000)
(2 rows)

postgres=# explain SELECT * FROM minervdb_bench  WHERE id < 200000;
                                        QUERY PLAN                                        
------------------------------------------------------------------------------------------
 Index Only Scan using idx_id on minervdb_bench  (cost=0.43..6550.25 rows=198961 width=4)
   Index Cond: (id < 200000)
(2 rows)

postgres=# explain SELECT count(*) FROM minervdb_bench GROUP BY id % 2;
                                     QUERY PLAN                                      
-------------------------------------------------------------------------------------
 GroupAggregate  (cost=1605360.71..1805360.25 rows=9999977 width=12)
   Group Key: ((id % 2))
   ->  Sort  (cost=1605360.71..1630360.65 rows=9999977 width=4)
         Sort Key: ((id % 2))
         ->  Seq Scan on minervdb_bench  (cost=0.00..169247.71 rows=9999977 width=4)
 JIT:
   Functions: 6
   Options: Inlining true, Optimization true, Expressions true, Deforming true
(8 rows)

Using pg_stat_statements

We can use pg_stat_statements to group the identical PostgreSQL queries by latency, To enable pg_stat_statements you have to add the following line to postgresql.conf and restart PostgreSQL server:

# postgresql.conf
shared_preload_libraries = 'pg_stat_statements'

pg_stat_statements.max = 10000
pg_stat_statements.track = all

Run “CREATE EXTENSION pg_stat_statements” in your database so that PostgreSQL will create a view for you:

postgres=# CREATE EXTENSION pg_stat_statements;
CREATE EXTENSION

postgres=# \d pg_stat_statements
                    View "public.pg_stat_statements"
       Column        |       Type       | Collation | Nullable | Default 
---------------------+------------------+-----------+----------+---------
 userid              | oid              |           |          | 
 dbid                | oid              |           |          | 
 queryid             | bigint           |           |          | 
 query               | text             |           |          | 
 calls               | bigint           |           |          | 
 total_time          | double precision |           |          | 
 min_time            | double precision |           |          | 
 max_time            | double precision |           |          | 
 mean_time           | double precision |           |          | 
 stddev_time         | double precision |           |          | 
 rows                | bigint           |           |          | 
 shared_blks_hit     | bigint           |           |          | 
 shared_blks_read    | bigint           |           |          | 
 shared_blks_dirtied | bigint           |           |          | 
 shared_blks_written | bigint           |           |          | 
 local_blks_hit      | bigint           |           |          | 
 local_blks_read     | bigint           |           |          | 
 local_blks_dirtied  | bigint           |           |          | 
 local_blks_written  | bigint           |           |          | 
 temp_blks_read      | bigint           |           |          | 
 temp_blks_written   | bigint           |           |          | 
 blk_read_time       | double precision |           |          | 
 blk_write_time      | double precision |           |          | 

postgres=#

pg_stat_statements view columns explained (Source: https://www.postgresql.org/docs/12/pgstatstatements.html)

You can list queries by latency / Response Time in PostgreSQL  by querying pg_stat_statements:

postgres=# \x
Expanded display is on.

select query,calls,total_time,min_time,max_time,mean_time,stddev_time,rows from pg_stat_statements order by mean_time desc;

-[ RECORD 1 ]---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
query       | SELECT count(*) FROM minervdb_bench GROUP BY id % $1
calls       | 6
total_time  | 33010.533078
min_time    | 4197.876021
max_time    | 6485.33594
mean_time   | 5501.755512999999
stddev_time | 826.3716429081501
rows        | 72
-[ RECORD 2 ]---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
query       | CREATE INDEX idx_id ON minervdb_bench(id)
calls       | 1
total_time  | 4560.808456
min_time    | 4560.808456
max_time    | 4560.808456
mean_time   | 4560.808456
stddev_time | 0
rows        | 0
-[ RECORD 3 ]---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
query       | ANALYZE
calls       | 1
total_time  | 441.725223
min_time    | 441.725223
max_time    | 441.725223
mean_time   | 441.725223
stddev_time | 0
rows        | 0

-[ RECORD 4 ]--------------------------------------------------------------------------------------------------------------------------------------------------------------------------
------------------------------------------------------------------------------------------------------------------------------------------------
query       | SELECT a.attname,                                                                                                                                                         
                                                                                                                                                +
            |   pg_catalog.format_type(a.atttypid, a.atttypmod),                                                                                                                        
                                                                                                                                                +
            |   (SELECT substring(pg_catalog.pg_get_expr(d.adbin, d.adrelid, $1) for $2)                                                                                                                                                                                                                                                +
            |    FROM pg_catalog.pg_attrdef d                                                                                                                                                                                                                                                                                           +
            |    WHERE d.adrelid = a.attrelid AND d.adnum = a.attnum AND a.atthasdef),                                                                                                                                                                                                                                                  +
            |   a.attnotnull,                                                                                                                                                                                                                                                                                                           +
            |   (SELECT c.collname FROM pg_catalog.pg_collation c, pg_catalog.pg_type t                                                                                                                                                                                                                                                 +
            |    WHERE c.oid = a.attcollation AND t.oid = a.atttypid AND a.attcollation <> t.typcollation) AS attcollation,                                                                                                                                                                                                             +
            |   a.attidentity,                                                                                                                                                                                                                                                                                                          +
            |   a.attgenerated                                                                                                                                                                                                                                                                                                          +
            | FROM pg_catalog.pg_attribute a                                                                                                                                                                                                                                                                                            +
            | WHERE a.attrelid = $3 AND a.attnum > $4 AND NOT a.attisdropped                                                                                                                                                                                                                                                            +
            | ORDER BY a.attnum
calls       | 4
total_time  | 1.053107
min_time    | 0.081565
max_time    | 0.721785
mean_time   | 0.26327675000000006
stddev_time | 0.2658756938743884
rows        | 86

If you already know the epicenter of the bottleneck is a particular query or event / time, you can reset statistics just before query / event to monitor the problematic components in the PostgreSQL performance, You can do that by just calling pg_stat_statements_reset() as copied below:

postgres= SELECT pg_stat_statements_reset();

Conclusion

Performance tuning is the process of optimizing PostgreSQL performance by streamlining the execution of multiple SQL statements. In other words, performance tuning simplifies the process of accessing and altering information contained by the database with the intention of improving query response times and database application operations.

The post Troubleshooting PostgreSQL Performance from Slow Queries appeared first on The WebScale Database Infrastructure Operations Experts.

Our founder and Principal Shiv Iyer did a webinar (July 09, 2020) on PostgreSQL Internals and Performance Optimization  , Shiv is a longtime Open Source Database Systems Operations expert with core expertise on performance optimization, capacity planning / sizing, architecture / internals, transaction processing engineering, horizontal scalability & partitioning, storage optimization, distributed database systems and data compression algorithms. The core objective of this webinar was to talk about PostgreSQL internals, troubleshooting PostgreSQL query performance, index optimization, partitioning, PostgreSQL configuration parameters and best practices. We strongly believe that understanding PostgreSQL architecture and internals are very important to troubleshoot PostgreSQL performance proactively and efficiently, You can download the PDF copy of the webinar here , If you want the recorded video of the webinar please contact support@minervadb.com .

Contact MinervaDB for Enterprise-Class PostgreSQL Consulting and 24*7 Consultative Support

The post MinervaDB Webinar: PostgreSQL Internals and Performance Optimization appeared first on The WebScale Database Infrastructure Operations Experts.

MinervaDB provides full-stack PostgreSQL consulting, support and managed Remote DBA Services for several customers globally addressing performance, scalability, high availability and database reliability engineering. Our prospective customers and other fellow DBAs are often curious about how we at MinervaDB troubleshoot PostgreSQL performance, So we thought will share the same through a webinar on “PostgreSQL Internals and Performance Troubleshooting“. This webinar is hosted by Shiv Iyer ( Founder and Principal of MinervaDB ), a longtime Open Source Database Systems Operations expert with core expertise on performance optimization, capacity planning / sizing, architecture / internals, transaction processing engineering, horizontal scalability & partitioning, storage optimization, distributed database systems and data compression algorithms. You can register for the webinar here

Contact MinervaDB for Enterprise-Class PostgreSQL Consulting and 24*7 Consultative Support 

The post PostgreSQL Internals and Performance Troubleshooting Webinar appeared first on The WebScale Database Infrastructure Operations Experts.

What is VACUUM in PostgreSQL ?

In PostgreSQL, whenever rows in a table deleted, The existing row or tuple is marked as dead ( will not be physically removed) and during an update, it marks corresponding exiting tuple as dead and inserts a new tuple so in PostgreSQL UPDATE operations = DELETE + INSERT. These dead tuples consumes unnecessary storage and eventually you have a bloated PostgreSQL database, This is a serious issue to solve for a PostgreSQL DBA. VACUUM reclaims in the storage occupied by dead tuples, Please keep this noted that reclaimed storage space is never given back to the resident operating system rather they are just defragmented within the same database page and so storage for reuse to future data inserts in the same table. Does the pain stops here ? No, It doesn’t. Bloat seriously affect the PostgreSQL query performance, In PostgreSQL tables and indexes are stored as array of fixed-size pages ( usually 8KB in size). Whenever a query requests for rows, the PostgreSQL instance loads these pages into the memory and dead rows causes expensive disk I/O during data loading.

Blame it on PostgreSQL Multi-Version Concurrency Control (MVCC) for bloating.  Because, Multi-Version Concurrency Control (MVCC) in PostgreSQL is committed to maintain each transaction isolated and durable ( ACID compliance in transaction management), readers never block writers and vice versa. Every transaction ( such as an insert, update or delete, as well as explicitely wrapping a group of statements together via BEGIN – COMMIT.) in PostgreSQL is identified by a transaction ID called XID. When a transaction starts, Postgres increments an XID and assigns it to the current transaction. PostgresSQL also stores transaction information on every row in the system, which is used to determine whether a row is visible to the transaction or not. Because different transactions will have visibility to a different set of rows, PostgreSQL needs to maintain potentially obsolete records. This is why an UPDATE actually creates a new row and why DELETE doesn’t really remove the row: it merely marks it as deleted and sets the XID values appropriately. As transactions complete, there will be rows in the database that cannot possibly be visible to any future transactions. These are called dead rows ( technically bloated records in PostgreSQL).

How PostgreSQL database handles bloating for optimal performance and storage efficiency ?

In the past PostgreSQL DBAs ( pre PostgreSQL 8 ) used to reclaim the storage from dead tuples by running VACUUM command manually, This was most daunting task to do because DBAs need to to balance the resource utilization for vacuuming against the current transaction volume / load to plan when to do it, and also when to abort it.  PostgreSQL “autovacuum” feature simplified DBA life much better on managing database bloating and vacuum.

How Autovacuum works in PostgreSQL ? 

Autovacuum is one of the background utility processes that starts automatically when the actual number of dead tuples in a table exceeds an effective threshold, due to updates and deletes, The frequency of this process is controlled by PostgreSQL configuration parameter autovacuum_naptime (default is 1 minute) and autovacuum attempts to start a new worker process every time when vacuuming process begins, this completely depends on the value of configuration parameter autovacuum_max_workers (default 3). The worker searches for tables where PostgreSQL’s statistics records indicate a large enough number of rows have changed relative to the table size. The formula is:

## The formula which is applied by autovacuum process to identify tables which are bloated and need immediate attention for vacuuming: 

[estimated rows invalidated] ≥ autovacuum_vacuum_scale_factor * [estimated table size] + autovacuum_vacuum_threshold

This is what happens internally with-in PostgreSQL during autovacuum process:

The worker threads generated will start removing dead tuples and compacting pages aggressively but these entire activities consumes intense disk I/O throughput, Autovacuum records these I/O credits and when it exceeds autovacuum_vacuum_cost_limit then autovacuum pauses all workers for few milliseconds depending on the value of configuration parameter autovacuum_vacuum_cost_delay (default is 20 milliseconds). The vacuuming as mentioned above is an resource hogging and time consuming activity because every vacuum worker scan through individual dead rows to remove index entries pointed on those rows before compaction of pages, If you have deployed PostgreSQL on a limited memory / RAM infrastructure then maintenance_work_mem configuration parameter will be very conservative and this leaves worker thread to process only limited dead rows during each attempt making vacuum fall behind.

How to configure Autovacuum parameters ?

The default autovacuum works great for few GBs PostgreSQL Database and is definitely not recommended for larger PostgreSQL infrastructure as with increasing data / transactions volume the vacuum will fall behind. Once vacuum has fallen behind, It will directly impact query execution plan and performance, This will direct DBAs to either run autovacuum less frequently or not at all. The following matrix recommends optimal sizing of configuration parameters for larger PostgreSQL database instances:

Interesting links for extra reading 

• https://www.citusdata.com/blog/2016/11/04/autovacuum-not-the-enemy/ 
• https://www.postgresql.org/docs/11/sql-vacuum.html 
• https://devcenter.heroku.com/articles/postgresql-concurrency 
• https://github.com/pgexperts/pgx_scripts/tree/master/bloat 

☛ MinervaDB contacts – Sales & General Inquiries

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MINERVADB FAX	+1 (209) 314-2364
MinervaDB Email - General / Sales / Consulting	contact@minervadb.com
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MinervaDB Email -Remote DBA	remotedba@minervadb.com
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The post How PostgreSQL Autovacuum works ? appeared first on The WebScale Database Infrastructure Operations Experts.

Before PostgreSQL  11 adding anew table column with a non-null default value results in a rewrite of the entire table, This works fine for a smaller data sets but the whole thing goes super complicated and expensive with high volume databases because of ACCESS EXCLUSIVE LOCK  ( default lock mode for LOCK TABLE statements that do not specify a mode explicitly ) on the table which conflicts with locks of all modes (ACCESS SHARE, ROW SHARE, ROW EXCLUSIVE, SHARE UPDATE EXCLUSIVE, SHARE, SHARE ROW EXCLUSIVE, EXCLUSIVE, and ACCESS EXCLUSIVE) and guarantees the holder of the lock is the only transaction accessing the table in any way, and it’ll block every other operation until it’s released; even simple SELECT statements have to wait, This is unacceptable for a continuously 24*7 accessed table and is a serious performance bottleneck. PostgreSQL 11 has addressed this problem gracefully by storing default value in the catalog and ushered whenever needed in rows exiting at the  time the change was made and for new rows / new versions of existing rows are written with default value in place. The rows which existed before this change was made  with NULL values uses the value stored in the catalog when the row is fetched. This makes adding new table columns with default values faster and even smarter. To conclude, The default value doesn’t have to be  a static expression, It can be even non-volatile expressions like CURRENT_TIMESTAMP but volatile expressions such as random(), currval(), timeofday() will still result in table rewrites.

If you want  MinervaDB PostgreSQL consultants to help you in PostgreSQL Performance Optimization and Tuning, Please book for an no obligation PostgreSQL consulting below:

The post How PostgreSQL 11 made adding new table columns with default values faster ? appeared first on The WebScale Database Infrastructure Operations Experts.

PostgreSQL 12 provides significant performance and maintenance enhancements to its indexing system and to partitioning. PostgreSQL 12 introduces the ability to run queries over JSON documents using JSON path expressions defined in the SQL/JSON standard. Such queries may utilize the existing indexing mechanisms for documents stored in the JSONB format to efficiently retrieve data. PostgreSQL 12 extends its support of ICU collations by allowing users to define “nondeterministic collations” that can, for example, allow case-insensitive or accent-insensitive comparisons. PostgreSQL 12 enhancements include notable improvements to query performance, particularly over larger data sets, and overall space utilization. This release provides application developers with new capabilities such as SQL/JSON path expression support, optimizations for how common table expression (WITH) queries are executed, and generated columns

The following are top three most interesting features introduced in PostgreSQL 12 : 

1. Much better indexing for performance and optimal space management in PostgreSQL 12 –  Why we worry so much about indexing in Database Systems ? All of us know very well that large amount data Can’t technically fit well in the main memory. When you have more number of keys, You will eventually end-up reading more from disk compared to main memory and Disk access time is very high compared to main memory access time.  We use B-tree indexes to reduce the number of disk accesses. B-tree is a data structure that store data in its node in sorted order. B-tree stores data in a way that each node accommodate keys in ascending order. B-tree uses an array of entries for a single node and having reference to child node for each of these entries. We spend significant amount of time to reclaim the storage occupied by dead tuples and this happen due to PostgreSQL indexes bloat, which take up extra storage in the disk. Thanks to PostgreSQL 12, We have now much better B-tree indexing which can reduce up to 40% in space utilization and overall gain in the query performance and that means we have now both faster WRITEs and READs. PostgreSQL 12 introduces the ability to rebuild indexes without blocking writes to an index via the REINDEX CONCURRENTLY command, allowing users to avoid downtime scenarios for lengthy index rebuilds.

2. ALTER TABLE ATTACH PARTITION without blocking queries – In PostgreSQL, Every lock has queue. If transaction T2 tries to acquire a lock that is already held by transaction T1  with a conflicting lock level, then transaction T1 will wait in the lock queue. Now something interesting happens: if another transaction T3 comes in, then it will not only have to check for conflict with T1, but also with transaction T2, and any other transaction in the lock queue. So even if your DDL command can run very quickly, it might be in a queue for a long time waiting for queries to finish, and queries that start after it will be blocked behind it. PostgreSQL support partitioning, The partitioning is about splitting logically one large table into several pieces. Partitioning improves query performance.  The PostgreSQL partitioning substitutes for leading columns of indexes, reducing index size and making it more likely that the heavily-used parts of the indexes fit in memory. Till PostgreSQL 11, During INSERTs into a partitioned table, the every partition of respective table was locked and it doesn’t even matter if it received a new record or not, At a large data operations scale with larger number of partitions this could become a serious bottleneck. Starting from PostgreSQL 12, When we are inserting a row, only the related partition will be locked. This results in much better performance at higher partition counts, especially when inserting just 1 row at a time.

3. JSON Path support in Postgres 12 – The JSON data-type was introduced in PostgreSQL-9.2 and from there PostgreSQL commitment to JSON data management has increased significantly.  The SQL:2016 standard introduced JSON and various ways to query JSON values, The major addition came-up in PostgreSQL-9.4 with the addition of JSONB data-type. JSONB is an advanced version of JSON data-type which stores the JSON data in binary format. PostgreSQL 12 support JSON Path, The JSON Path in PostgreSQL is implemented as jsonpath data type, which is actually the binary representation of parsed SQL/JSON path expression. The main task of the path language is to specify the parts (the projection) of JSON data to be retrieved by path engine for the SQL/JSON query functions. PostgreSQL 12 introduces the ability to run queries over JSON documents using JSON path expressions defined in the SQL/JSON standard. Such queries may utilize the existing indexing mechanisms for documents stored in the JSONB format to efficiently retrieve data.

References 

1. Gentle Guide to JSONPATH in PostgreSQL – https://github.com/obartunov/sqljsondoc/blob/master/jsonpath.md
2. PostgreSQL release notes – https://www.postgresql.org/about/news/1976/

Book for an no obligation consulting with MinervaDB PostgreSQL Team 

The post Top Three Most Compelling New Features From PostgreSQL 12 appeared first on The WebScale Database Infrastructure Operations Experts.