PostgreSQL® Archives - credativ®

The PostgreSQL 2024Q1 back-branch releases 16.2, 15.6, 14.11, 13.14 and 12.18 on February 8th 2024. Besides fixing a security issue (CVE-2024-0985) and the usual bugs, they are somewhat unique in that they address two performance problems by backporting fixes already introduced into the master branch before. In this blog post, we describe two quick benchmarks that show how the new point releases have improved. The benchmarks were done on a ThinkPad T14s Gen 3 which has a Intel i7-1280P CPU with 20 cores and 32 GB of RAM.

Scalability Improvements During Heavy Contention

The performance improvements in the 2024Q1 point releases concerns locking scalability improvements at high client counts, i.e., when the system is under heavy contention. Benchmarks had shown that the performance was getting worse dramatically for a pgbench run with more than 128 clients. The original commit to master (which subsequently was released with version 16) is from November 2022. It got introduced into the back-branches now as version 16 has seen some testing and the results were promising.

The benchmark we used is adapted from this post by the patch author and consists of a tight pgbench run simply executing SELECT txid_current() for five seconds each at increasing client count and measuring the transactions per second:

$ cat /tmp/txid.sql
SELECT txid_current();
$ for c in 1 2 4 8 16 32 64 96 128 192 256 384 512 768 1024 1536;
> do echo -n "$c ";pgbench -n -M prepared -f /tmp/txid.sql -c$c -j$c -T5 2>&1|grep '^tps'|awk '{print $3}';
> done

The following graph shows the average transactions per second (tps) over 3 runs with increasing client count (1-1536 clients), using the Debian 12 packages for version 15, comparing the 2023Q4 release (15.5, package postgresql-15_15.5-0+deb12u1) with the 2024Q1 release (15.6, package postgresql-15_15.6-0+deb12u1):

The tps numbers are basically the same up to 128 clients, whereas afterwards the 15.5 transaction counts drops from the peak of 650k 10-fold to 65k. The new 15.6 release maintains the transaction count much better and still maintains around 300k tps at the 1536 clients, which is a 4.5-fold increase of the 2024Q1 release compared to previously.

This benchmark is of course a best-case, artificial scenario, but it shows that the latest point release of Postgres can improve scalability dramatically for heavily contested locking scenarios.

JIT Memory Consumption Improvements

JIT (just-in-time compilation with LLVM) was introduced in version 11 of Postgres and made the default in version 13. For a long time now, it has been known that long-running PostgreSQL sessions that run JIT queries repeatedly leak memory. There have been several bug reports about this, including some more in the Debian bug tracker and probably elsewhere.

This has been diagnosed to be due to JIT inlining and a work-around is setting jit_inline_above_cost to -1 from the default value of 500000. However, this disables JIT inlining completely. The 2024Q1 back-branch releases contain a backport of a change that will go into version 17: after every 100 queries, the LLVM caches are dropped and recreated, plugging the memory leak.

To show how the memory consumption has improved, we use the test case from this bug report. The benchmark is prepared as followed:

   id integer NOT NULL,
   CONSTRAINT leak_test_pkey PRIMARY KEY (id)

INSERT INTO leak_test(id)
   SELECT id
   FROM generate_series(1,100000) id

Then, the process ID of the backend is noted and the SQL query mentioned in the bug report run 5000 times in a loop:

=> SELECT pg_backend_pid();

=> DO $$DECLARE loop_cnt integer;
->   loop_cnt := 5000;
->   LOOP
->     PERFORM
->       id,
->       (SELECT count(*) FROM leak_test x WHERE as x_result,
->       (SELECT count(*) FROM leak_test y WHERE as y_result
->       /* Leaks memory around 80 kB on each query, but only if two sub-queries are used. */
->     FROM leak_test l;
->     loop_cnt := loop_cnt - 1;
->     EXIT WHEN loop_cnt = 0;
->   END LOOP;
-> END$$;

During this the memory consumption of the Postgres backend is recorded via pidstat:

pidstat -r -hl -p 623404 2 | tee -a leak_test.log.15.6
Linux 6.1.0-18-amd64 (     15.02.2024  _x86_64_    (20 CPU)

# Time        UID       PID  minflt/s  majflt/s     VSZ     RSS   %MEM  Command
12:48:56      118    623404      0,00      0,00  381856   91504   0,28  postgres: 15/main: postgres postgres [local] SELECT
12:48:58      118    623404      0,00      0,00  381856   91504   0,28  postgres: 15/main: postgres postgres [local] SELECT
12:49:00      118    623404      0,00      0,00  381856   91504   0,28  postgres: 15/main: postgres postgres [local] SELECT
12:49:02      118    623404      0,00      0,00  381856   91504   0,28  postgres: 15/main: postgres postgres [local] SELECT
12:49:04      118    623404   7113,00      0,00  393632  109252   0,34  postgres: 15/main: postgres postgres [local] SELECT
12:49:06      118    623404  13219,00      0,00  394556  109508   0,34  postgres: 15/main: postgres postgres [local] SELECT
12:49:08      118    623404  14376,00      0,00  395384  108228   0,33  postgres: 15/main: postgres postgres [local] SELECT

The benchmark are again repeated for the 15.5 and 15.6 Debian 12 packages (which are both linked against LLVM-14) and the RSS memory consumption as reported by pidstat is plotted against time:

While the memory consumption of the 15.5 session increases linearly over time from 100 to 600 MB, it stays more or less constant at around 100 MB for 15.6. This is a great improvement that will make JIT much more usable for larger installations with long running sessions where so far the usual recommendation has been to disable JIT entirely.


The 2024Q1 patch release has important performance improvements for lock scalability and JIT memory consumption that we have demonstrated in this blog post. Furthermore, the patch release contains other important bug fixes and a security fix for CVE-2024-0985. This security issue is limited to materialized views and a admin user needs to be tricked into recreating a malicious materialized view on behalf of an attacker. But it has seen some german press coverage so quite a few of our customers were especially made aware of it and asked us to assist them with their minor upgrades. In general, Postgres patch releases are low-risk and unintrusive (just install the updated packages and restart the Postgres instances if the package did not do this itself) so that they should always be deployed as soon as possible.

Moodle is a popular Open Source online learning platform. Especially since the beginning of the COVID-19 pandemic the importance of Moodle for schools and universities has further increased. In some states in Germany all schools had to switch to Moodle and other platforms like BigBlueButton in the course of a few days. This leads to scalability problems if suddenly several tens of thousands of pupils need to access Moodle.
Besides scaling the Moodle application itself, the database needs to be considered as well. One of the database options for Moodle is PostgreSQL. In this blog post, we present load-balancing options for Moodle using PostgreSQL.

High-Availability via Patroni

An online learning platform can be considered critical infrastructure from the point of view of the educational system and should be made highly available, in particular the database. A good solution for PostgreSQL is Patroni, we reported on its Debian-integration in the past.

In short, Patroni uses a distributed consensus store (DCS) to elect a leader from a typically 3-node cluster or initiate a failover and elect a new leader in the case of a leader failure, without entering a split-brain scenario. In addition, Patroni provides a REST API used for communication among nodes and from the patronictl program, e.g. to change the Postgres configuration online on all nodes or to initiate a switchover.

Client-solutions for high availability

From Moodle’s perspective, however, it must additionally be ensured that it is connected to the leader, otherwise no write transactions are possible. Traditional high-availability solutions such as Pacemaker use virtual IPs (VIPs) here, which are pivoted to the new primary node in the event of a failover. For Patroni there is the vip-manager project instead, which monitors the leader key in the DCS and sets or removes cluster VIP locally. This is also integrated into Debian as well.

An alternative is to use client-side failover based on PostgreSQL’s libpq library. For this, all cluster members are listed in the connection string and the connection option target_session_attrs=read-write is added. Configured this way, if a connection is broken, the client will try to reach the other nodes until a new primary is found.

Another option is HAProxy, a highly scalable TCP/HTTP load balancer. By performing periodic health checks on Patroni‘s REST API of each node, it can determine the current leader and forward client queries to it.

Moodle database configuration

Moodle’s connection to a PostgreSQL database is configured in config.php, e.g. for a simple stand-alone database:

$CFG->dbtype    = 'pgsql';
$CFG->dblibrary = 'native';
$CFG->dbhost    = '';
$CFG->dbname    = 'moodle';
$CFG->dbuser    = 'moodle';
$CFG->dbpass    = 'moodle';
$CFG->prefix    = 'mdl_';
$CFG->dboptions = array (
  'dbport' => '',
  'dbsocket' => ''

The default port 5432 is used here.

If streaming replication is used, the standbys can additionally be defined as readonly and assigned to an own database user (which only needs read permissions):

$CFG->dboptions = array (
  'readonly' => [
    'instance' => [
      'dbhost' => '',
      'dbport' =>  '',
      'dbuser' => 'moodle_safereads',
      'dbpass' => 'moodle'
      'dbhost' => '',
      'dbport' =>  '',
      'dbuser' => 'moodle_safereads',
      'dbpass' => 'moodle'

Failover/load balancing with libpq

If a highly available Postgres cluster is used with Patroni, the primary, as described above, can be switched to prevent loss of data or shutdown of the system, in case of a failover or switchover incident. Moodle does not provide a way to set generic database options here and thus setting target_session_attrs=read-write directly is not possible. Therefore we developed a patch for this and implemented it in the Moodle tracker. This allows the additional option 'dbfailover' => 1, in the $CFG->dboptions array, which adds the necessary connection option target_session_attrs=read-write. A customized config.php would look like this:

$CFG->dbtype    = 'pgsql';
$CFG->dblibrary = 'native';
$CFG->dbhost    = ',,';
$CFG->dbname    = 'moodle';
$CFG->dbuser    = 'moodle';
$CFG->dbpass    = 'moodle';
$CFG->prefix    = 'mdl_';
$CFG->dboptions = array (
  'dbfailover' => 1,
  'dbport' => '',
  'dbsocket' => '',
  'readonly' => [
    'instance' => [
      'dbhost' => '',
      'dbport' =>  '',
      'dbuser' => 'moodle_safereads',
      'dbpass' => 'moodle'
      'dbhost' => '',
      'dbport' =>  '',
      'dbuser' => 'moodle_safereads',
      'dbpass' => 'moodle'
      'dbhost' => '',
      'dbport' =>  '',
      'dbuser' => 'moodle_safereads',
      'dbpass' => 'moodle'

Failover/load balancing with HAProxy

If HAProxy is to be used instead, then $CFG->dbhost must be set to the HAProxy host e.g. in case HAProxy is running locally on the Moodle server(s). Moreover a second port (e.g. 65432) can be defined for read queries, which is configured as readonly in $CFG->dboptions, same as the streaming replication standby above. The config.php would then look like this:

$CFG->dbtype    = 'pgsql';
$CFG->dblibrary = 'native';
$CFG->dbhost    = '';
$CFG->dbname    = 'moodle';
$CFG->dbuser    = 'moodle';
$CFG->dbpass    = 'moodle';
$CFG->prefix    = 'mdl_';
$CFG->dboptions = array (
  'dbport' => '',
  'dbsocket' => '',
  'readonly' => [
    'instance' => [
      'dbhost' => '',
      'dbport' =>  '65432',
      'dbuser' => 'moodle_safereads',
      'dbpass' => 'moodle'

The HAProxy configuration file haproxy.cfg can look like the following example:

    maxconn 100

    log global
    mode tcp
    retries 2
    timeout client 30m
    timeout connect 4s
    timeout server 30m
    timeout check 5s

listen stats
    mode http
    bind *:7000
    stats enable
    stats uri /

listen postgres_write
    bind *:5432
    mode tcp
    option httpchk
    http-check expect status 200
    default-server inter 3s fall 3 rise 3 on-marked-down shutdown-sessions
    server pg1 maxconn 100 check port 8008
    server pg2 maxconn 100 check port 8008
    server pg3 maxconn 100 check port 8008

HAProxy expects incoming write connections (postgres_write) on port 5432 and forwards them to port 5432 of the cluster members. The primary is determined by an HTTP check on port 8008 (the default Patroni REST API port); Patroni returns status 200 here for the primary and status 503 for standbys.

For read queries (postgres_read), it must be decided whether the primary should also serve read-only queries or not. If this is the case, a simple Postgres check (pgsql-check) can be used; however, this may lead to entries in the PostgreSQL log regarding incorrect or incomplete logins:

listen postgres_read
    bind *:65432
    mode tcp
    balance leastconn
    option pgsql-check user haproxy
    default-server inter 3s fall 3 rise 3 on-marked-down shutdown-sessions
    server pg1 check
    server pg2 check
    server pg3 check

If you don’t want the primary to participate in the read scaling you can simply use the same HTTP check as in the postgres_write section, this time expecting HTTP status 503:

listen postgres_read
    bind *:65432
    mode tcp
    balance leastconn
    option httpchk
    http-check expect status 503
    default-server inter 3s fall 3 rise 3 on-marked-down shutdown-sessions
    server pg1 check port 8008
    server pg2 check port 8008
    server pg3 check port 8008

Revised Ansible playbook

HAProxy support has also been implemented in version 0.3 of our Ansible playbooks for automated setup of a three-node PostgreSQL Patroni cluster on Debian. The new variable haproxy_primary_read_scale can be used to decide whether HAProxy should also issue requests on the read-only port to the primary node or only to the followers.

We are happy to help!

Whether it’s PostgreSQL, Patroni, HAProxy, Moodle, or any other open source software; with over 22+ years of development and service experience in the open source space, credativ GmbH can assist you with unparalleled and individually customizable support. We are there to help and assist you in all your open source infrastructure needs – if desired 24 hours a day, 365 days a year!

We look forward to hearing from you.

SQLreduce: Reduce verbose SQL queries to minimal examples

Developers often face very large SQL queries that raise some errors. SQLreduce is a tool to reduce that complexity to a minimal query.

SQLsmith generates random SQL queries

SQLsmith is a tool that generates random SQL queries and runs them against a PostgreSQL server (and other DBMS types). The idea is that by fuzz-testing the query parser and executor, corner-case bugs can be found that would otherwise go unnoticed in manual testing or with the fixed set of test cases in PostgreSQL’s regression test suite. It has proven to be an effective tool with over 100 bugs found in different areas in the PostgreSQL server and other products since 2015, including security bugs, ranging from executor bugs to segfaults in type and index method implementations. For example, in 2018, SQLsmith found that the following query triggered a segfault in PostgreSQL:

  case when pg_catalog.lastval() < pg_catalog.pg_stat_get_bgwriter_maxwritten_clean() then case when pg_catalog.circle_sub_pt(
          cast(cast(null as circle) as circle),
          cast((select location from public.emp limit 1 offset 13)
             as point)) ~ cast(nullif(case when cast(null as box) &> (select boxcol from public.brintest limit 1 offset 2)
                 then (select f1 from public.circle_tbl limit 1 offset 4)
               else (select f1 from public.circle_tbl limit 1 offset 4)
          case when (select pg_catalog.max(class) from public.f_star)
                 ~~ ref_0.c then cast(null as circle) else cast(null as circle) end
            ) as circle) then ref_0.a else ref_0.a end
       else case when pg_catalog.circle_sub_pt(
          cast(cast(null as circle) as circle),
          cast((select location from public.emp limit 1 offset 13)
             as point)) ~ cast(nullif(case when cast(null as box) &> (select boxcol from public.brintest limit 1 offset 2)
                 then (select f1 from public.circle_tbl limit 1 offset 4)
               else (select f1 from public.circle_tbl limit 1 offset 4)
          case when (select pg_catalog.max(class) from public.f_star)
                 ~~ ref_0.c then cast(null as circle) else cast(null as circle) end
            ) as circle) then ref_0.a else ref_0.a end
       end as c0,
  case when (select intervalcol from public.brintest limit 1 offset 1)
         >= cast(null as "interval") then case when ((select pg_catalog.max(roomno) from
             !~~ ref_0.c)
        and (cast(null as xid) <> 100) then ref_0.b else ref_0.b end
       else case when ((select pg_catalog.max(roomno) from
             !~~ ref_0.c)
        and (cast(null as xid) <> 100) then ref_0.b else ref_0.b end
       end as c1,
  ref_0.a as c2,
  (select a from public.idxpart1 limit 1 offset 5) as c3,
  ref_0.b as c4,
      cast((select pg_catalog.sum(float4col) from public.brintest)
         as float4)) over (partition by ref_0.a,ref_0.b,ref_0.c order by ref_0.b) as c5,
  cast(nullif(ref_0.b, ref_0.a) as int4) as c6, ref_0.b as c7, ref_0.c as c8
  public.mlparted3 as ref_0
where true;

However, just like in this 40-line, 2.2kB example, the random queries generated by SQLsmith that trigger some error are most often very large and contain a lot of noise that does not contribute to the error. So far, manual inspection of the query and tedious editing was required to reduce the example to a minimal reproducer that developers can use to fix the problem.

Reduce complexity with SQLreduce

This issue is solved by SQLreduce. SQLreduce takes as input an arbitrary SQL query which is then run against a PostgreSQL server. Various simplification steps are applied, checking after each step that the simplified query still triggers the same error from PostgreSQL. The end result is a SQL query with minimal complexity.

SQLreduce is effective at reducing the queries from original error reports from SQLsmith to queries that match manually-reduced queries. For example, SQLreduce can effectively reduce the above monster query to just this:

SELECT pg_catalog.stddev(NULL) OVER () AS c5 FROM public.mlparted3 AS ref_0

Note that SQLreduce does not try to derive a query that is semantically identical to the original, or produces the same query result – the input is assumed to be faulty, and we are looking for the minimal query that produces the same error message from PostgreSQL when run against a database. If the input query happens to produce no error, the minimal query output by SQLreduce will just be SELECT.

How it works

We’ll use a simpler query to demonstrate how SQLreduce works and which steps are taken to remove noise from the input. The query is bogus and contains a bit of clutter that we want to remove:

$ psql -c 'select pg_database.reltuples / 1000 from pg_database, pg_class where 0 < pg_database.reltuples / 1000 order by 1 desc limit 10'
ERROR:  column pg_database.reltuples does not exist

Let’s pass the query to SQLreduce:

$ sqlreduce 'select pg_database.reltuples / 1000 from pg_database, pg_class where 0 < pg_database.reltuples / 1000 order by 1 desc limit 10'

SQLreduce starts by parsing the input using pglast and libpg_query which expose the original PostgreSQL parser as a library with Python bindings. The result is a parse tree that is the basis for the next steps. The parse tree looks like this:

├── targetList
│   └── /
│       ├── pg_database.reltuples
│       └── 1000
├── fromClause
│   ├── pg_database
│   └── pg_class
├── whereClause
│   └── <
│       ├── 0
│       └── /
│           ├── pg_database.reltuples
│           └── 1000
├── orderClause
│   └── 1
└── limitCount
    └── 10

Pglast also contains a query renderer that can render back the parse tree as SQL, shown as the regenerated query below. The input query is run against PostgreSQL to determine the result, in this case ERROR: column pg_database.reltuples does not exist.

Input query: select pg_database.reltuples / 1000 from pg_database, pg_class where 0 < pg_database.reltuples / 1000 order by 1 desc limit 10
Regenerated: SELECT pg_database.reltuples / 1000 FROM pg_database, pg_class WHERE 0 < ((pg_database.reltuples / 1000)) ORDER BY 1 DESC LIMIT 10
Query returns: ✔ ERROR:  column pg_database.reltuples does not exist

SQLreduce works by deriving new parse trees that are structurally simpler, generating SQL from that, and run these queries against the database. The first simplification steps work on the top level node, where SQLreduce tries to remove whole subtrees to quickly find a result. The first reduction tried is to remove LIMIT 10:

SELECT pg_database.reltuples / 1000 FROM pg_database, pg_class WHERE 0 < ((pg_database.reltuples / 1000)) ORDER BY 1 DESC ✔

The query result is still ERROR: column pg_database.reltuples does not exist, indicated by a ✔ check mark. Next, ORDER BY 1 is removed, again successfully:

SELECT pg_database.reltuples / 1000 FROM pg_database, pg_class WHERE 0 < ((pg_database.reltuples / 1000)) ✔

Now the entire target list is removed:

SELECT FROM pg_database, pg_class WHERE 0 < ((pg_database.reltuples / 1000)) ✔

This shorter query is still equivalent to the original regarding the error message returned when it is run against the database. Now the first unsuccessful reduction step is tried, removing the entire FROM clause:

SELECT WHERE 0 < ((pg_database.reltuples / 1000)) ✘ ERROR:  missing FROM-clause entry for table "pg_database"

That query is also faulty, but triggers a different error message, so the previous parse tree is kept for the next steps. Again a whole subtree is removed, now the WHERE clause:

SELECT FROM pg_database, pg_class ✘ no error

We have now reduced the input query so much that it doesn’t error out any more. The previous parse tree is still kept which now looks like this:

├── fromClause
│   ├── pg_database
│   └── pg_class
└── whereClause
    └── <
        ├── 0
        └── /
            ├── pg_database.reltuples
            └── 1000

Now SQLreduce starts digging into the tree. There are several entries in the FROM clause, so it tries to shorten the list. First, pg_database is removed, but that doesn’t work, so pg_class is removed:

SELECT FROM pg_class WHERE 0 < ((pg_database.reltuples / 1000)) ✘ ERROR:  missing FROM-clause entry for table "pg_database"
SELECT FROM pg_database WHERE 0 < ((pg_database.reltuples / 1000)) ✔

Since we have found a new minimal query, recursion restarts at top-level with another try to remove the WHERE clause. Since that doesn’t work, it tries to replace the expression with NULL, but that doesn’t work either.

SELECT FROM pg_database ✘ no error
SELECT FROM pg_database WHERE NULL ✘ no error

Now a new kind of step is tried: expression pull-up. We descend into WHERE clause, where we replace A < B first by A and then by B.

SELECT FROM pg_database WHERE 0 ✘ ERROR:  argument of WHERE must be type boolean, not type integer
SELECT FROM pg_database WHERE pg_database.reltuples / 1000 ✔
SELECT WHERE pg_database.reltuples / 1000 ✘ ERROR:  missing FROM-clause entry for table "pg_database"

The first try did not work, but the second one did. Since we simplified the query, we restart at top-level to check if the FROM clause can be removed, but it is still required.

From A / B, we can again pull up A:

SELECT FROM pg_database WHERE pg_database.reltuples ✔
SELECT WHERE pg_database.reltuples ✘ ERROR:  missing FROM-clause entry for table "pg_database"

SQLreduce has found the minimal query that still raises ERROR: column pg_database.reltuples does not exist with this parse tree:

├── fromClause
│   └── pg_database
└── whereClause
    └── pg_database.reltuples

At the end of the run, the query is printed along with some statistics:

Minimal query yielding the same error:
SELECT FROM pg_database WHERE pg_database.reltuples

Pretty-printed minimal query:
FROM pg_database
WHERE pg_database.reltuples

Seen: 15 items, 915 Bytes
Iterations: 19
Runtime: 0.107 s, 139.7 q/s

This minimal query can now be inspected to fix the bug in PostgreSQL or in the application.

About credativ

The credativ GmbH is a manufacturer-independent consulting and service company located in Moenchengladbach, Germany. With over 22+ years of development and service experience in the open source space, credativ GmbH can assist you with unparalleled and individually customizable support. We are here to help and assist you in all your open source infrastructure needs.

Since the successful merger with Instaclustr in 2021, credativ GmbH has been the European headquarters of the Instaclustr Group, which helps organizations deliver applications at scale through its managed platform for open source technologies such as Apache Cassandra®, Apache Kafka®, Apache Spark™, Redis™, OpenSearch®, PostgreSQL®, and Cadence.
Instaclustr combines a complete data infrastructure environment with hands-on technology expertise to ensure ongoing performance and optimization. By removing the infrastructure complexity, we enable companies to focus internal development and operational resources on building cutting edge customer-facing applications at lower cost. Instaclustr customers include some of the largest and most innovative Fortune 500 companies.

Congratulations to the Debian Community

The Debian Project just released version 11 (aka “bullseye”) of their free operating system. In total, over 6,208 contributors worked on this release and were indispensable in making this launch happen. We would like to thank everyone involved for their combined efforts, hard work, and many hours pent in recent years building this new release that will benefit the entire open source community.

We would also like to acknowledge our in-house Debian developers who contributed to this effort. We really appreciate the work you do on behalf of the community and stand firmly behind your contributions.

What’s New in Debian 11 Bullseye

Debian 11 comes with a number of meaningful changes and enhancements. The new release includes over 13,370 new software packages, for a total of over 57,703 packages on release. Out of these, 35,532 packages have been updated to newer versions, including an update in the kernel from 4.19 in “buster” to 5.10 in bullseye.

Bullseye expands on the capabilities of driverless printing with Common Unix Printing System (CUPS) and driverless scanning with Scanner Access Now Easy (SANE). While it was possible to use CUPS for driverless printing with buster, bullseye comes with the package ipp-usb, which allows a USB device to be treated as a network device and thus extend driverless printing capabilities. SANE connects to this when set up correctly and connected to a USB port.

As in previous releases, Debian 11 comes with a Debian Edu / Skolelinux version. Debian Edu has been a complete solution for schools for many years. Debian Edu can provide the entire network for a school and then only users and machines need to be added after installation. This can also be easily managed via the web interface GOsa².

Debian 11 bullseye can be downloaded here.

For more information and greater technical detail on the new Debian 11 release, please refer to the official release notes on

Contributions by Instaclustr Employees

Our Debian roots run deep here. credativ, which was acquired by Instaclustr in March 2021, has always been an active part of the Debian community and visited every DebConf since 2004. Debian also serves as the operating system at the heart of the Instaclustr Managed Platform.

For the release of Debian 11, our team has taken over various responsibilities in the community. Our contributions include:

Many of our colleagues have made significant contributions to the current release, including:

How to Upgrade

Given that Debian 11 bullseye is a major release, we suggest that everyone running on Debian 10 buster upgrade. The main steps for an upgrade include:

  1. Make sure to backup any data that should not get lost and prepare for recovery
  2. Remove non-Debian packages and clean up leftover files and old versions
  3. Upgrade to latest point release
  4. Check and prepare your APT source-list files by adding the relevant Internet sources or local mirrors
  5. Upgrade your packages and then upgrade your system

You can find a more detailed walkthrough of the upgrade process in the Debian documentation.

All existing credativ customers who are running a Debian-based installation are naturally covered by our service and support and are encouraged to reach out.

If you are interested in upgrading from your old Debian version, or if you have questions with regards to your Debian infrastructure, do not hesitate to drop us an email or contact us at

Or, you can get started in minutes with any one of these open source technologies like Apache Cassandra, Apache Kafka, Redis, and OpenSearch on the Instaclustr Managed Platform. Sign up for a free trial today.

100% Open Source – 100% Cost Control

The PostgreSQL® Competence Center of the credativ Group announces the creation of a new comprehensive service and support package that includes all services necessary for the operation of PostgreSQL® in enterprise environments. This new offering will be available starting August 1st, 2020.

“Motivated by the requirements of many of our customers, we put together a new and comprehensive PostgreSQL® service package that meets all the requirements for the operation of PostgreSQL® in enterprise environments.”, says Dr. Michael Meskes, managing director of the credativ Group.

“In particular, this package focuses on true open source support, placing great emphasis on the absence of any proprietary elements in our offer. Despite this, our service package still grants all of the necessary protection for operation in business-critical areas. Additionally, with this new offering, the number of databases operated within the company’s environment does not matter. As a result, credativ offers 100% cost control while allowing the entire database environment to be scaled as required.”

Database operation in enterprise environments places very high demands on the required service and support. Undoubtedly an extremely powerful, highly scalable, and rock-solid relational database is the basis for secure and high-performance operation.

However, a complete enterprise operating environment consists of much more than just the pure database; one needs holistic lifecycle management. Major and Minor version upgrades, migrations, security, services, patch management, and Long-Term Support (LTS) are just a few essential factors. Additionally, staying up to date also requires continuous regular training and practice.

Services for the entire operating environment

Beyond the database itself, one also needs a stable and highly scalable operating environment providing all necessary Open Source tools for PostgreSQL and meeting all requirements regarding high availability, security, performance, database monitoring, backups, and central orchestration of the entire database infrastructure. These tools include the open-source versions of numerous PostgreSQL related project packages, such as pgAdmin, pgBadger, pgBackrest, Patroni, but also the respective operating system environment and popular projects like Prometheus and Grafana, or even cloud infrastructures based on Kubernetes.

Just as indispensable as the accurate functioning of the database is smooth interaction with any components connected with the database. Therefore it is important to include and consider these components as well. Only when all aspects, such as operating system, load balancer, web server, application server, or PostgreSQL cluster solutions, work together, will the database achieve optimal performance.

This new support package is backed up by continuous 24×7 enterprise support, with guaranteed Service Level Agreements and all necessary services for the entire database environment, including a comprehensive set of open-source tools commonly used in today’s enterprise PostgreSQL environments. All of these requirements are covered by the PostgreSQL Enterprise package from credativ and are included within the scope of services. The new enterprise service proposal is offered at an annual flat rate, additionally simplifying costs and procurement.

About credativ

The credativ Group is an independent consulting and services company with primary locations in Germany, the United States, and India.

Since 1999, credativ has focused entirely on the planning and implementation of professional business solutions using Open Source software. Since May 2006, credativ operates the Open Source Support Center (OSSC), offering professional 24×7 enterprise support for numerous Open Source projects.

In addition, our PostgreSQL Competence Center of credativ provides a dedicated database team a comprehensive service for the PostgreSQL object-relational database eco-system.

This article was originally written by Philip Haas.

Open Source Summit is the world’s largest, all-encompassing open source conference. Topics such as the latest infrastructure software, development on the Linux kernel and current works in open source communities are discussed. Until now, open source databases were a missing part of the conference program.

However, databases such as PostgreSQL® or Apache Cassandra are one of the most important pillars in modern open source infrastructures.

Together with the Linux Foundation, we are pleased to announce that this year’s Open Source Summit North America and Europe each will have their own database track.

Together with Sunil Kamath (Microsoft) and Divya Bhargov (Pivotal), our Managing Director Dr. Michael Meskes forms the program committee for the new track of the event.

Dr. Michael Meskes comments this in his blog post at the Linux Foundation as follows:

“The open source database track will feature topics specific to databases themselves and their integration to the computing backbone for applications. The track will focus on databases of all kinds, as long as they are open source, and any deployment and integration topics.”

The complete blog post of the Linux Foundation can be found here.

The Linux Foundation and the program committee are looking forward to plenty of submissions for this track. Presentations can be submitted until 16 February (North America) and 14 June (Europe).

This year’s Open Source Summit North America will take place in Austin, Texas. Whereas the Open Source Summit Europe will be hosted in Dublin, Ireland. Both events are once again backed by credativ with a sponsorship.

This article was originally written by Philip Haas.

Patroni is a clustering solution for PostgreSQL® that is getting more and more popular in the cloud and Kubernetes sector due to its operator pattern and integration with Etcd or Consul. Some time ago we wrote a blog post about the integration of Patroni into Debian. Recently, the vip-manager project which is closely related to Patroni has been uploaded to Debian by us. We will present vip-manager and how we integrated it into Debian in the following.

To recap, Patroni uses a distributed consensus store (DCS) for leader-election and failover. The current cluster leader periodically updates its leader-key in the DCS. As soon the key cannot be updated by Patroni for whatever reason it becomes stale. A new leader election is then initiated among the remaining cluster nodes.

PostgreSQL Client-Solutions for High-Availability

From the user’s point of view it needs to be ensured that the application is always connected to the leader, as no write transactions are possible on the read-only standbys. Conventional high-availability solutions like Pacemaker utilize virtual IPs (VIPs) that are moved to the primary node in the case of a failover.

For Patroni, such a mechanism did not exist so far. Usually, HAProxy (or a similar solution) is used which does periodic health-checks on each node’s Patroni REST-API and routes the client requests to the current leader.

An alternative is client-based failover (which is available since PostgreSQL 10), where all cluster members are configured in the client connection string. After a connection failure the client tries each remaining cluster member in turn until it reaches a new primary.


A new and comfortable approach to client failover is vip-manager. It is a service written in Go that gets started on all cluster nodes and connects to the DCS. If the local node owns the leader-key, vip-manager starts the configured VIP. In case of a failover, vip-manager removes the VIP on the old leader and the corresponding service on the new leader starts it there. The clients are configured for the VIP and will always connect to the cluster leader.

Debian-Integration of vip-manager

For Debian, the pg_createconfig_patroni program from the Patroni package has been adapted so that it can now create a vip-manager configuration:

pg_createconfig_patroni 11 test --vip=

Similar to Patroni, we start the service for each instance:

systemctl start vip-manager@11-test

The output of patronictl shows that pg1 is the leader:

| Cluster | Member |    Host    |  Role  |  State  | TL | Lag in MB |
| 11-test |  pg1   | | Leader | running |  1 |           |
| 11-test |  pg2   |  |        | running |  1 |         0 |
| 11-test |  pg3   | |        | running |  1 |         0 |

In journal of ‘pg1’ it can be seen that the VIP has been configured:

Jan 19 14:53:38 pg1 vip-manager[9314]: 2020/01/19 14:53:38 IP address state is false, desired true
Jan 19 14:53:38 pg1 vip-manager[9314]: 2020/01/19 14:53:38 Configuring address on eth0
Jan 19 14:53:38 pg1 vip-manager[9314]: 2020/01/19 14:53:38 IP address state is true, desired true

If LXC containers are used, one can also see the VIP in the output of lxc-ls -f:

pg1     RUNNING 0         -, -    false
pg2     RUNNING 0         -            -    false
pg3     RUNNING 0         -           -    false

The vip-manager packages are available for Debian testing (bullseye) and unstable, as well as for the upcoming 20.04 LTS Ubuntu release (focal) in the official repositories. For Debian stable (buster), as well as for Ubuntu 19.04 and 19.10, packages are available at maintained by credativ, along with the updated Patroni packages with vip-manager integration.

Switchover Behaviour

In case of a planned switchover, e.g. pg2 becomes the new leader:

# patronictl -c /etc/patroni/11-test.yml switchover --master pg1 --candidate pg2 --force
Current cluster topology
| Cluster | Member |    Host    |  Role  |  State  | TL | Lag in MB |
| 11-test |  pg1   | | Leader | running |  1 |           |
| 11-test |  pg2   |  |        | running |  1 |         0 |
| 11-test |  pg3   | |        | running |  1 |         0 |
2020-01-19 15:35:32.52642 Successfully switched over to "pg2"
| Cluster | Member |    Host    |  Role  |  State  | TL | Lag in MB |
| 11-test |  pg1   | |        | stopped |    |   unknown |
| 11-test |  pg2   |  | Leader | running |  1 |           |
| 11-test |  pg3   | |        | running |  1 |         0 |

The VIP has now been moved to the new leader:

pg1     RUNNING 0         -          -    false
pg2     RUNNING 0         -, -    false
pg3     RUNNING 0         -          -    false

This can also be seen in the journals, both from the old leader:

Jan 19 15:35:31 pg1 patroni[9222]: 2020-01-19 15:35:31,634 INFO: manual failover: demoting myself
Jan 19 15:35:31 pg1 patroni[9222]: 2020-01-19 15:35:31,854 INFO: Leader key released
Jan 19 15:35:32 pg1 vip-manager[9314]: 2020/01/19 15:35:32 IP address state is true, desired false
Jan 19 15:35:32 pg1 vip-manager[9314]: 2020/01/19 15:35:32 Removing address on eth0
Jan 19 15:35:32 pg1 vip-manager[9314]: 2020/01/19 15:35:32 IP address state is false, desired false

As well as from the new leader pg2:

Jan 19 15:35:31 pg2 patroni[9229]: 2020-01-19 15:35:31,881 INFO: promoted self to leader by acquiring session lock
Jan 19 15:35:31 pg2 vip-manager[9292]: 2020/01/19 15:35:31 IP address state is false, desired true
Jan 19 15:35:31 pg2 vip-manager[9292]: 2020/01/19 15:35:31 Configuring address on eth0
Jan 19 15:35:31 pg2 vip-manager[9292]: 2020/01/19 15:35:31 IP address state is true, desired true
Jan 19 15:35:32 pg2 patroni[9229]: 2020-01-19 15:35:32,923 INFO: Lock owner: pg2; I am pg2

As one can see, the VIP is moved within one second.

Updated Ansible Playbook

Our Ansible-Playbook for the automated setup of a three-node cluster on Debian has also been updated and can now configure a VIP if so desired:

# ansible-playbook -i inventory -e vip= patroni.yml

Questions and Help

Do you have any questions or need help? Feel free to write to

The PostgreSQL® Global Development Group (PGDG) has released version 12 of the popular free PostgreSQL® database. As our article for Beta 4 has already indicated, a number of new features, improvements and optimizations have been incorporated into the release. These include among others:

Optimized disk space utilization and speed for btree indexes

btree-Indexes, the default index type in PostgreSQL®, has experienced some optimizations in PostgreSQL® 12.

btree indexes used to store duplicates (multiple entries with the same key values) in an unsorted order. This has resulted in suboptimal use of physical representation in these indexes. An optimization now stores these multiple key values in the same order as they are physically stored in the table. This improves disk space utilization and the effort required to manage corresponding btree type indexes. In addition, indexes with multiple indexed columns use an improved physical representation so that their storage utilization is also improved. To take advantage of this in PostgreSQL® 12, however, if they were upgraded to the new version using pg_upgrade via a binary upgrade, these indexes must be recreated or re-indexed.

Insert operations in btree indexes are also accelerated by improved locking.

Improvements for pg_checksums

credativ has contributed an extension for pg_checksums that allows to enable or disable block checksums in stopped PostgreSQL® instances. Previously, this could only be done by recreating the physical data representation of the cluster using initdb.
pg_checksums now has the option to display a status history on the console with the --progress parameter. The corresponding code contributions come from the colleagues Michael Banck and Bernd Helmle.

Optimizer Inlining of Common Table Expressions

Up to and including PostgreSQL® 11, the PostgreSQL® Optimizer was unable to optimize common table expressions (also called CTE or WITH queries). If such an expression was used in a query, the CTE was always evaluated and materialized first before the rest of the query was processed. This resulted in expensive execution plans for more complex CTE expressions. The following generic example illustrates this. A join is given with a CTE expression that filters all even numbers from a numeric column:

WITH t_cte AS (SELECT id FROM foo WHERE id % 2 = 0) SELECT COUNT(*) FROM t_cte JOIN bar USING(id);

In PostgreSQL® 11, using a CTE always leads to a CTE scan that materializes the CTE expression first:

                                                       QUERY PLAN                                                        
 Aggregate  (cost=2231.12..2231.14 rows=1 width=8) (actual time=48.684..48.684 rows=1 loops=1)
   Buffers: shared hit=488
   CTE t_cte
     ->  Seq Scan on foo  (cost=0.00..1943.00 rows=500 width=4) (actual time=0.055..17.146 rows=50000 loops=1)
           Filter: ((id % 2) = 0)
           Rows Removed by Filter: 50000
           Buffers: shared hit=443
   ->  Hash Join  (cost=270.00..286.88 rows=500 width=0) (actual time=7.297..47.966 rows=5000 loops=1)
         Hash Cond: ( =
         Buffers: shared hit=488
         ->  CTE Scan on t_cte  (cost=0.00..10.00 rows=500 width=4) (actual time=0.063..31.158 rows=50000 loops=1)
               Buffers: shared hit=443
         ->  Hash  (cost=145.00..145.00 rows=10000 width=4) (actual time=7.191..7.192 rows=10000 loops=1)
               Buckets: 16384  Batches: 1  Memory Usage: 480kB
               Buffers: shared hit=45
               ->  Seq Scan on bar  (cost=0.00..145.00 rows=10000 width=4) (actual time=0.029..3.031 rows=10000 loops=1)
                     Buffers: shared hit=45
 Planning Time: 0.832 ms
 Execution Time: 50.562 ms
(19 rows)

This plan first materializes the CTE with a sequential scan with a corresponding filter (id % 2 = 0). Here no functional index is used, therefore this scan is correspondingly more expensive. Then the result of the CTE is linked to the table bar by Hash Join with the corresponding Join condition. With PostgreSQL® 12, the optimizer now has the ability to inline these CTE expressions without prior materialization. The underlying optimized plan in PostgreSQL® 12 will look like this:

                                                                QUERY PLAN                                                                 
 Aggregate  (cost=706.43..706.44 rows=1 width=8) (actual time=9.203..9.203 rows=1 loops=1)
   Buffers: shared hit=148
   ->  Merge Join  (cost=0.71..706.30 rows=50 width=0) (actual time=0.099..8.771 rows=5000 loops=1)
         Merge Cond: ( =
         Buffers: shared hit=148
         ->  Index Only Scan using foo_id_idx on foo  (cost=0.29..3550.29 rows=500 width=4) (actual time=0.053..3.490 rows=5001 loops=1)
               Filter: ((id % 2) = 0)
               Rows Removed by Filter: 5001
               Heap Fetches: 10002
               Buffers: shared hit=74
         ->  Index Only Scan using bar_id_idx on bar  (cost=0.29..318.29 rows=10000 width=4) (actual time=0.038..3.186 rows=10000 loops=1)
               Heap Fetches: 10000
               Buffers: shared hit=74
 Planning Time: 0.646 ms
 Execution Time: 9.268 ms
(15 rows)

The advantage of this method is that there is no initial materialization of the CTE expression. Instead, the query is executed directly with a Join. This works for all non-recursive CTE expressions without side effects (for example, CTEs with write statements) and those that are referenced only once per query. The old behavior of the optimizer can be forced with the WITH ... AS MATERIALIZED ... directive.

Generated Columns

Generated Columns in PostgreSQL® 12 are materialized columns, which calculate a result based on expressions using existing column values. These are stored with the corresponding result values in the tuple. The advantage is that there is no need to create triggers for subsequent calculation of column values. The following simple example illustrates the new functionality using a price table with net and gross prices:

CREATE TABLE preise(netto numeric,
                    brutto numeric GENERATED ALWAYS AS (netto * 1.19) STORED);
INSERT INTO preise VALUES(17.30);
INSERT INTO preise VALUES(19.15);
SELECT * FROM preise;
 netto │ brutto
 17.30 │ 20.5870
   225 │  267.75
   247 │  293.93
 19.15 │ 22.7885
(4 rows)

The column brutto is calculated directly from the net price. The keyword STORED is mandatory. Of course, indexes can also be created on Generated Columns, but they cannot be part of a primary key. Furthermore, the SQL expression must be unique, i.e. it must return the same result even if the input quantity is the same. Columns declared as Generated Columns cannot be used explicitly in INSERT or UPDATE operations. If a column list is absolutely necessary, the corresponding value can be indirectly referenced with the keyword DEFAULT.

Omission of explicit OID columns

Explicit OID columns have historically been a way to create unique column values so that a table row can be uniquely identified database-wide. However, for a long time PostgreSQL® has only created these explicitly and considered their basic functionality obsolete. With PostgreSQL® the possibility to create such columns explicitly is now finally abolished. This means that it will no longer be possible to specify the WITH OIDS directive for tables. System tables that have always referenced OID objects uniquely will now return OID values without explicitly specifying OID columns in the result set. Especially older software, which handled catalog queries carelessly, could get problems with a double column output.

Moving recovery.conf to postgresql.conf

Up to and including PostgreSQL® 11, database recovery and streaming replication instances were configured via a separate configuration file recovery.conf.

With PostgreSQL® 12, all configuration work done there now migrates to postgresql.conf. The recovery.conf file is no longer required. PostgreSQL® 12 refuses to start as soon as this file exists. Whether recovery or streaming standby is desired is now decided either by a recovery.signal file (for recovery) or by a standby.signal file (for standby systems). The latter has priority if both files are present. The old parameter standby_mode, which controlled this behavior since then, has been removed.

For automatic deployments of high-availability systems, this means a major change. However, it is now also possible to perform corresponding configuration work almost completely using the ALTER SYSTEM command.


With PostgreSQL® 12 there is now a way to re-create indexes with as few locks as possible. This greatly simplifies one of the most common maintenance tasks in very write-intensive databases. Previously, a combination of CREATE INDEX CONCURRENTLY and DROP INDEX CONCURRENTLY had to be used. In doing so, it was also necessary to ensure that index names were reassigned accordingly, if required.

The release notes give an even more detailed overview of all new features and above all incompatibilities with previous PostgreSQL® versions.

Yesterday, the fourth beta of the upcoming PostgreSQL®-major version 12 was released.

Compared to its predecessor PostgreSQL® 11, there are many new features:

Of course, PostgreSQL® 12 will be tested using sqlsmith, the SQL “fuzzer” from our colleague Andreas Seltenreich. Numerous bugs in different PostgreSQL® versions were found with sqlsmith by using randomly generated SQL queries.

Debian and Ubuntu packages for PostgreSQL® 12 are going to be published on with credativ’s help. This work will be handled by our colleague Christoph Berg.

The release of PostgreSQL® 12 is expected in the next weeks.

In this article we will look at the highly available operation of PostgreSQL® in a Kubernetes environment. A topic that is certainly of particular interest to many of our PostgreSQL® users.

Together with our partner company MayaData, we will demonstrate below the application possibilities and advantages of the extremely powerful open source project – OpenEBS.

OpenEBS is a freely available storage management system, whose development is supported and backed by MayaData.

We would like to thank Murat-Karslioglu from MayaData and our colleague Adrian Vondendriesch for this interesting and helpful article. This article simultaneously also appeared on

PostgreSQL® anywhere — via Kubernetes with some help from OpenEBS and credativ engineering

by Murat Karslioglu, OpenEBS and Adrian Vondendriesch, credativ


If you are already running Kubernetes on some form of cloud whether on-premises or as a service, you understand the ease-of-use, scalability and monitoring benefits of Kubernetes — and you may well be looking at how to apply those benefits to the operation of your databases.

PostgreSQL® remains a preferred relational database, and although setting up a highly available Postgres cluster from scratch might be challenging at first, we are seeing patterns emerging that allow PostgreSQL® to run as a first class citizen within Kubernetes, improving availability, reducing management time and overhead, and limiting cloud or data center lock-in.

There are many ways to run high availability with PostgreSQL®; for a list, see the PostgreSQL® Documentation. Some common cloud-native Postgres cluster deployment projects include Crunchy Data’s, Sorint.lab’s Stolon and Zalando’s Patroni/Spilo. Thus far we are seeing Zalando’s operator as a preferred solution in part because it seems to be simpler to understand and we’ve seen it operate well.

Some quick background on your authors:

  • OpenEBS is a broadly deployed OpenSource storage and storage management project sponsored by MayaData.
  • credativ is a leading open source support and engineering company with particular depth in PostgreSQL®.

In this blog, we’d like to briefly cover how using cloud-native or “container attached” storage can help in the deployment and ongoing operations of PostgreSQL® on Kubernetes. This is the first of a series of blogs we are considering — this one focuses more on why users are adopting this pattern and future ones will dive more into the specifics of how they are doing so.

At the end you can see how to use a Storage Class and a preferred operator to deploy PostgreSQL® with OpenEBS underlying

If you are curious about what container attached storage of CAS is you can read more from the Cloud Native Computing Foundation (CNCF) here.

Conceptually you can think of CAS as being the decomposition of previously monolithic storage software into containerized microservices that themselves run on Kubernetes. This gives all the advantages of running Kubernetes that already led you to run Kubernetes — now applied to the storage and data management layer as well. Of special note is that like Kubernetes, OpenEBS runs anywhere so the same advantages below apply whether on on-premises or on any of the many hosted Kubernetes services.

PostgreSQL® plus OpenEBS

®-with-OpenEBS-persistent-volumes.png”>Postgres-Operator (for cluster deployment)

  • Docker installed
  • Kubernetes 1.9+ cluster installed
  • kubectl installed
  • OpenEBS installed
  • Install OpenEBS

    1. If OpenEBS is not installed in your K8s cluster, this can be done from here. If OpenEBS is already installed, go to the next step.
    2. Connect to MayaOnline (Optional): Connecting the Kubernetes cluster to MayaOnline provides good visibility of storage resources. MayaOnline has various support options for enterprise customers.

    Configure cStor Pool

    1. If cStor Pool is not configured in your OpenEBS cluster, this can be done from here. As PostgreSQL® is a StatefulSet application, it requires a single storage replication factor. If you prefer additional redundancy you can always increase the replica count to 3.
      During cStor Pool creation, make sure that the maxPools parameter is set to >=3. If a cStor pool is already configured, go to the next step. Sample YAML named openebs-config.yaml for configuring cStor Pool is provided in the Configuration details below.


    #Use the following YAMLs to create a cStor Storage Pool.
    # and associated storage class.
    kind: StoragePoolClaim
     name: cstor-disk
     name: cstor-disk
     type: disk
     poolType: striped
     # NOTE — Appropriate disks need to be fetched using `kubectl get disks`
     # `Disk` is a custom resource supported by OpenEBS with `node-disk-manager`
     # as the disk operator
    # Replace the following with actual disk CRs from your cluster `kubectl get disks`
    # Uncomment the below lines after updating the actual disk names.
    # Replace the following with actual disk CRs from your cluster from `kubectl get disks`
    # — disk-184d99015253054c48c4aa3f17d137b1
    # — disk-2f6bced7ba9b2be230ca5138fd0b07f1
    # — disk-806d3e77dd2e38f188fdaf9c46020bdc
    # — disk-8b6fb58d0c4e0ff3ed74a5183556424d
    # — disk-bad1863742ce905e67978d082a721d61
    # — disk-d172a48ad8b0fb536b9984609b7ee653
     — -

    Create Storage Class

    1. You must configure a StorageClass to provision cStor volume on a cStor pool. In this solution, we are using a StorageClass to consume the cStor Pool which is created using external disks attached on the Nodes. The storage pool is created using the steps provided in the Configure StoragePool section. In this solution, PostgreSQL® is a deployment. Since it requires replication at the storage level the cStor volume replicaCount is 3. Sample YAML named openebs-sc-pg.yaml to consume cStor pool with cStorVolume Replica count as 3 is provided in the configuration details below.


    kind: StorageClass
      name: openebs-postgres
      annotations: cstor |
          - name: StoragePoolClaim
            value: "cstor-disk"
          - name: ReplicaCount
            value: "3"       
    reclaimPolicy: Delete

    Launch and test Postgres Operator

    1. Clone Zalando’s Postgres Operator.
    git clone
    cd postgres-operator

    Use the OpenEBS storage class

    1. Edit manifest file and add openebs-postgres as the storage class.
    nano manifests/minimal-postgres-manifest.yaml

    After adding the storage class, it should look like the example below:

    apiVersion: ""
    kind: postgresql
      name: acid-minimal-cluster
      namespace: default
      teamId: "ACID"
        size: 1Gi
        storageClass: openebs-postgres
      numberOfInstances: 2
        # database owner
        - superuser
        - createdb
    # role for application foo
        foo_user: []
    #databases: name->owner
        foo: zalando
        version: "10"
          shared_buffers: "32MB"
          max_connections: "10"
          log_statement: "all"

    Start the Operator

    1. Run the command below to start the operator
    kubectl create -f manifests/configmap.yaml # configuration
    kubectl create -f manifests/operator-service-account-rbac.yaml # identity and permissions
    kubectl create -f manifests/postgres-operator.yaml # deployment

    Create a Postgres cluster on OpenEBS

    Optional: The operator can run in a namespace other than default. For example, to use the test namespace, run the following before deploying the operator’s manifests:

    kubectl create namespace test
    kubectl config set-context $(kubectl config current-context) — namespace=test
    1. Run the command below to deploy from the example manifest:
    kubectl create -f manifests/minimal-postgres-manifest.yaml

    2. It only takes a few seconds to get the persistent volume (PV) for the pgdata-acid-minimal-cluster-0 up. Check PVs created by the operator using the kubectl get pv command:

    $ kubectl get pv
    pvc-8852ceef-48fe-11e9–9897–06b524f7f6ea 1Gi RWO Delete Bound default/pgdata-acid-minimal-cluster-0 openebs-postgres 8m44s
    pvc-bfdf7ebe-48fe-11e9–9897–06b524f7f6ea 1Gi RWO Delete Bound default/pgdata-acid-minimal-cluster-1 openebs-postgres 7m14s

    Connect to the Postgres master and test

    1. If it is not installed previously, install psql client:
    sudo apt-get install postgresql-client

    2. Run the command below and note the hostname and host port.

    kubectl get service — namespace default |grep acid-minimal-cluster

    3. Run the commands below to connect to your PostgreSQL® DB and test. Replace the [HostPort] below with the port number from the output of the above command:

    export PGHOST=$(kubectl get svc -n default -l application=spilo,spilo-role=master -o jsonpath="{.items[0].spec.clusterIP}")
    export PGPORT=[HostPort]
    export PGPASSWORD=$(kubectl get secret -n default postgres.acid-minimal-cluster.credentials -o ‘jsonpath={.data.password}’ | base64 -d)
    psql -U postgres -c ‘create table foo (id int)’

    Congrats you now have the Postgres-Operator and your first test database up and running with the help of cloud-native OpenEBS storage.

    Partnership and future direction

    As this blog indicates, the teams at MayaData / OpenEBS and credativ are increasingly working together to help organizations running PostgreSQL® and other stateful workloads. In future blogs, we’ll provide more hands-on tips.

    We are looking for feedback and suggestions on where to take this collaboration. Please provide feedback below or find us on Twitter or on the OpenEBS slack community.

    Patroni is a PostgreSQL high availability solution with a focus on containers and Kubernetes. Until recently, the available Debian packages had to be configured manually and did not integrate well with the rest of the distribution. For the upcoming Debian 10 “Buster” release, the Patroni packages have been integrated into Debian’s standard PostgreSQL framework by credativ. They now allow for an easy setup of Patroni clusters on Debian or Ubuntu.

    Patroni employs a “Distributed Consensus Store” (DCS) like Etcd, Consul or Zookeeper in order to reliably run a leader election and orchestrate automatic failover. It further allows for scheduled switchovers and easy cluster-wide changes to the configuration. Finally, it provides a REST interface that can be used together with HAProxy in order to build a load balancing solution. Due to these advantages Patroni has gradually replaced Pacemaker as the go-to open-source project for PostgreSQL high availability.

    However, many of our customers run PostgreSQL on Debian or Ubuntu systems and so far Patroni did not integrate well into those. For example, it does not use the postgresql-common framework and its instances were not displayed in pg_lsclusters output as usual.

    Integration into Debian

    In a collaboration with Patroni lead developer Alexander Kukushkin from Zalando the Debian Patroni package has been integrated into the postgresql-common framework to a large extent over the last months. This was due to changes both in Patroni itself as well as additional programs in the Debian package. The current Version 1.5.5 of Patroni contains all these changes and is now available in Debian “Buster” (testing) in order to setup Patroni clusters.

    The packages are also available on and thus installable on Debian 9 “Stretch” and Ubuntu 18.04 “Bionic Beaver” LTS for any PostgreSQL version from 9.4 to 11.

    The most important part of the integration is the automatic generation of a suitable Patroni configuration with the pg_createconfig_patroni command. It is run similar to pg_createcluster with the desired PostgreSQL major version and the instance name as parameters:

    pg_createconfig_patroni 11 test

    This invocation creates a file /etc/patroni/11-test.yml, using the DCS configuration from /etc/patroni/dcs.yml which has to be adjusted according to the local setup. The rest of the configuration is taken from the template /etc/patroni/ which is usable in itself but can be customized by the user according to their needs. Afterwards the Patroni instance is started via systemd similar to regular PostgreSQL instances:

    systemctl start patroni@11-test

    A simple 3-node Patroni cluster can be created and started with the following few commands, where the nodes pg1, pg2 and pg3 are considered to be hostnames and the local file dcs.yml contains the DCS configuration:

    for i in pg1 pg2 pg3; do ssh $i 'apt -y install postgresql-common'; done
    for i in pg1 pg2 pg3; do ssh $i 'sed -i "s/^#create_main_cluster = true/create_main_cluster = false/" /etc/postgresql-common/createcluster.conf'; done
    for i in pg1 pg2 pg3; do ssh $i 'apt -y install patroni postgresql'; done
    for i in pg1 pg2 pg3; do scp ./dcs.yml $i:/etc/patroni; done
    for i in pg1 pg2 pg3; do ssh @$i 'pg_createconfig_patroni 11 test' && systemctl start patroni@11-test'; done

    Afterwards, you can get the state of the Patroni cluster via

    ssh pg1 'patronictl -c /etc/patroni/11-patroni.yml list'
    | Cluster | Member |    Host    |  Role  |  State  | TL | Lag in MB |
    | 11-test |  pg1   | | Leader | running |  1 |           |
    | 11-test |  pg2   |  |        | stopped |    |   unknown |
    | 11-test |  pg3   |  |        | stopped |    |   unknown |

    Leader election has happened and pg1 has become the primary. It created its instance with the Debian-specific pg_createcluster_patroni program that runs pg_createcluster in the background. Then the two other nodes clone from the leader using the pg_clonecluster_patroni program which sets up an instance using pg_createcluster and then runs pg_basebackup from the primary. After that, all nodes are up and running:

    | Cluster | Member |    Host    |  Role  |  State  | TL | Lag in MB |
    | 11-test |  pg1   | | Leader | running |  1 |         0 |
    | 11-test |  pg2   |  |        | running |  1 |         0 |
    | 11-test |  pg3   |  |        | running |  1 |         0 |

    The well-known Debian postgresql-common commands work as well:

    ssh pg1 'pg_lsclusters'
    Ver Cluster Port Status Owner    Data directory                 Log file
    11  test    5432 online postgres /var/lib/postgresql/11/test    /var/log/postgresql/postgresql-11-test.log

    Failover Behaviour

    If the primary is abruptly shutdown, its leader token will expire after a while and Patroni will eventually initiate failover and a new leader election:

    | Cluster | Member |    Host   | Role |  State  | TL | Lag in MB |
    | 11-test |  pg2   | |      | running |  1 |         0 |
    | 11-test |  pg3   | |      | running |  1 |         0 |
    | Cluster | Member |    Host   |  Role  |  State  | TL | Lag in MB |
    | 11-test |  pg2   | | Leader | running |  2 |         0 |
    | 11-test |  pg3   | |        | running |  1 |         0 |
    | Cluster | Member |    Host   |  Role  |  State  | TL | Lag in MB |
    | 11-test |  pg2   | | Leader | running |  2 |         0 |
    | 11-test |  pg3   | |        | running |  2 |         0 |

    The old primary will rejoin the cluster as standby once it is restarted:

    | Cluster | Member |    Host    |  Role  |  State  | TL | Lag in MB |
    | 11-test |  pg1   | |        | running |    |   unknown |
    | 11-test |  pg2   |  | Leader | running |  2 |         0 |
    | 11-test |  pg3   |  |        | running |  2 |         0 |
    | Cluster | Member |    Host    |  Role  |  State  | TL | Lag in MB |
    | 11-test |  pg1   | |        | running |  2 |         0 |
    | 11-test |  pg2   |  | Leader | running |  2 |         0 |
    | 11-test |  pg3   |  |        | running |  2 |         0 |

    If a clean rejoin is not possible due to additional transactions on the old timeline the old primary gets re-cloned from the current leader. In case the data is too large for a quick re-clone, pg_rewind can be used. In this case a password needs to be set for the postgres user and regular database connections (as opposed to replication connections) need to be allowed between the cluster nodes.

    Creation of additional Instances

    It is also possible to create further clusters with pg_createconfig_patroni, one can either assign a PostgreSQL port explicitly via the --port option, or let pg_createconfig_patroni assign the next free port as is known from pg_createcluster:

    for i in pg1 pg2 pg3; do ssh $i 'pg_createconfig_patroni 11 test2 && systemctl start patroni@11-test2'; done
    ssh pg1 'patronictl -c /etc/patroni/11-test2.yml list'
    | Cluster  | Member |       Host      |  Role  |  State  | TL | Lag in MB |
    | 11-test2 |  pg1   | | Leader | running |  1 |         0 |
    | 11-test2 |  pg2   | |        | running |  1 |         0 |
    | 11-test2 |  pg3   | |        | running |  1 |         0 |

    Ansible Playbook

    In order to easily deploy a 3-node Patroni cluster we have created an Ansible playbook on Github. It automates the installation and configuration of PostgreSQL and Patroni on the three nodes, as well as the DCS server on a fourth node.

    Questions and Help

    Do you have any questions or need help? Feel free to write to

    This week version 1.3 of our PostgreSQL® appliance Elephant Shed was released.

    The highlight of the new version is support for Red Hat Enterprise Linux 7 and CentOS 7. As is already the case for Debian, the appliance heavily relies on the postgresql-common infrastructure which was previously ported to RPM.
    The well-known PostgreSQL® RPM packages from are integrated into the system via pg_createcluster and can be administrated from the Elephant Shed web interface.

    All other Elephant Shed components like pgAdmin4, Grafana, Prometheus, pgbackrest, Cockpit or shellinabox work in the same way as in the Debian version of the appliance. Only the SELinux functionality has to be deactivated in order to run pgAdmin4 and shellinabox as their packages do not support this.

    Besides the port to RPM the appliance infrastructure was updated. The Prometheus node-exporter is now available in version 0.16 in which many metric names were adjusted to the Prometheus naming scheme. The Grafana dashboard was updated accordingly. The Apache configuration was switched from authnz_pam to authnz_external as the former is not available on CentOS and stable functionality could not longer be guaranteed on Debian Buster.

    The next items on the Elephant-Shed roadmap are the integration of the REST-API in order to control particular components, as well as multi-host support so that several Elephant-Shed instances can be controlled simultaneously.
    An overhaul of the user interface is planned as well.

    The updated packages are available for download at If Elephant-Shed was installed already, the updates are provided via apt as usual.

    The open-source PostgreSQL® appliance Elephant-Shed is developed by credativ and is increasingly popular, as the most important compontents for the administration of PostgreSQL® servers are already included. Adjustments and extensions can be done at any time.

    Comprehensible technical support for Elephant-Shed is offered by credativ including guaranteed service-level agreements and optional 365 days and 24/7 hours.