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

☛ MinervaDB is trusted by top companies worldwide

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Karthik.P.R of Mydbops spoke about how MySQL shell can be used for Database Infrastructure Operations on MinervaDB Athena 2020, His topic was “MySQL Shell for Database Engineers”. The talk covered MySQL shell tools like upgrade checker, JSON import utility, table export utility, parallel table import utility, dump loading utility etc. You can download the PDF of his talk here

The post MinervaDB Athena 2020 – Karthik.P.R appeared first on The WebScale Database Infrastructure Operations Experts.

Peter Zaitsev (Founder and CEO of Percona) spoke in MinervaDB Athena 2020 about MySQL 8 observability, Troubleshooting MySQL performance proactively. In this talk, Peter share about what are MySQL performance metrics we should consider for building optimal MySQL infrastructure performance addressing performance, scalability and reliability.

⊗ MySQL 8 Observability by Peter Zaitsev

The post MinervaDB Athena 2020 – MySQL 8 Observability by Peter Zaitsev appeared first on The WebScale Database Infrastructure Operations Experts.

Anandamoy Roychowdhary (a.k.a Roy) talked about how Venture Capital companies like Sequoia Capital can help you in building successfully Database Systems companies both strategically and technically. In this talk, Roy shared the most common metrics followed by Sequoia Capital to measure business from seed stage to the growth capital phase with more insights to how open source projects get evolved to wider adoption, community development and institutionalization of the business.

⊗ Talk from Roy on how venture capital companies can help you to build the Database Systems startups / business for scale

The post MinervaDB Athena 2020 -Role of Venture Capital Companies in building Database Systems Companies by Anandamoy Roychowdhary appeared first on The WebScale Database Infrastructure Operations Experts.

Troubleshooting by Tanel Poder

In Athena 2020, Tanel Poder talked about troubleshooting Linux operations performance based on the detailed forensics and evidence collection through0x.tools (Linux Process Snapper from Tanel), Which is a free, open source /proc file system sampling tool which annotates Linux thread handling activities more intuitively. The talk was really interesting for Open Source Database Systems folks who attended the conference because they spend most of their professional hours troubleshooting performance of their Database Infrastructure Operations for optimal performance and also in this talk Tanel’s approach was bottom-up (interpreting performance metrics with Linux probes) compared top-down approach (depending on Database Systems metadata views), You can download the PDF of the talk here

⊗ Profiling Linux Operations for Performance and vTroubleshooting by Tanel Poder

The post MinervaDB Athena 2020 – Profiling Linux Operations for Performance and Troubleshooting by Tanel Poder appeared first on The WebScale Database Infrastructure Operations Experts.