Quick guide to Multi-master Replication in PostgreSQL

A while ago, I wrote a post about multi-master replication using symmetricDS. My scenario consists of a system with multiple nodes, all of them writing in their copies of the database. Sometimes the nodes may be offline, but I would like the system to be *eventually consistent*.

SymmetricDS is a Java-based framework that supports a number of RDBMS, including PostgreSQL, the one that I use. I don’t particularly like the fact that it uses Java: specially the UI, seems slow and unresponsive. However, the fact that the application is cross-platform is quite “handy”, as we can have the databases running in a number of different, and “talking to each other”.
SymmetricDS itself is free and Open Source (GPL). However, if you want to use the configuration GUI, there is a commercial product called “SymmetricDS Pro”. I could not find out how much the pro version costs (they are quite secretive in the website), but since I was in a bit of a rush to setup the synchronization system, I decided to try it out.
Previously, I evaluated the FOSS version, and was able to synchronize 2 databases on Ubuntu systems: what they called a “Standard 2 Tier Configuration”. This time, I went for a slightly more complicated scenario: synchronizing three different databases, all in different hosts, with a mix of Windows and Linux systems. With the help of the “pro” GUI, and the “Quick-start manual”, it took me less than two days to do it, which I think is ok.

Before start reading this post, please note that database replication is a complicated issue. Multi-master asynchronous replication is *definitely* complicated, with many things involved, so don’t expect the configuration to be a simple wizard. To be able to use it you need to understand well a series of concepts, that won’t take you five minutes. Having said this, “SymmetricDS Pro” does a pretty good job in helping a person that *has this concepts*, performing that task.

My case study, is a real world scenario where I have three different hosts running copies of my application and database. However it may be over-simplified, since I am doing simple operations with the application (inserting/updating data with all the nodes online). Asynchronous multi-master replication “gives space” for the rise of conflicts, and although SymmetricDS does provide some support for dealing with conflicts, this is a highly sensitive topic, that must be dealt on a “case-to-case” basis, by a person with a good knowledge of the domain. On my case study, I did not arrive to any conflicts so I won’t evaluate how symmetriCDS deals with them. Please have this issue in mind, if you decide to adopt SymmetricDS.

SymmetricDS Pro is not free, but you may download it and evaluate it for 30 days:


It is essential to give your email address, where they will provide you with the key to “unlock” the full functionality. I found it very easy to install it, following the instructions on the quick-start guide:

Click to access SymmetricDSPro-QuickStart-3.5.pdf

The only dependency is the Java Runtime Environment (JRE), which very likely you will already have running on your system, anyway.
In the guide they mention a “single-homed” scenario, where you will have a single instance of symmetricDS running and a “multi-homed” scenario, where you install a copy of symmetricDS for each host/database. Since I wanted to approach a “deployment scenario” with remote computers I went straight to the “multi-homed”. However, if you just want to test it, you may try the “single-homed” scenario (which is supported in the manual).

Although SymmetricDS enables a distributed system, you need to create a node that acts as a “registration server”. This node has to exist, even if you can make the other nodes “talk” to each other. Although it is ok if this node is offline for a while, I would pick a host that is mostly online (like a actual server).

I started by installing symmetriCDS in my “server” node. The installation is exactly the same on any node and when you finish, you start running the daemon (running something like “/symmetricDS/bin/sym”), and then run the node setup.

If you have installed symmetricDS on port 31415 (the default non-secure port), the configuration console can be run from pointing your browser to this address:


Since I was on the server host, I choose to setup a “server” node. SymmetricDS presents you with two “ready made” configurations, and an option to create your own, called: “I’ll configure things myself”. This is actually a very important step of your configuration, since it will define the architecture of the system (how many nodes you have, how they connect to each other, etc); later you may refine the configuration options, but the first decision is made here, so it is important to think well. Since I was a bit intimidated by the “I’ll configure things myself” option, and the “Standard 2 Tier Configuration” is the only one supported in the manual, I decided to go for this one first. If you are looking for a sort of tutorial, I would recommend this one, in order to check that everything is working on your system, etc.

Although they “claim” in the manual that the “client” group may correspond to many nodes, connected to one server, I found out that I could only make each client to talk to the server (and vice-verse), but I could not make the client nodes to talk to each other. It was like they were subscribing the “news” from the server, but the “news” that were arriving to the server via other nodes were not actually considered as “news”.

After that, I decided to try the “Multiple Sources to One Target Configuration”, which is also described as “Data Warehousing”. This was not exactly what I was looking for, but I was able to modify the architecture, until I arrived to something that suited me (and that I will describe later). The next screens, let you define the database connection string, and the url for communicating with the SymmetricDS instance; in my case:


(where invislaptop resolves to my server’s IP address)

After this, you are taken to the configuration dashboard, that should be “unlocked”, by using the key provided by email. The next thing you want to do, is to go to the “configuration” section. This section is very powerful, at the same time that is complicated and it allows you to tune and refine every aspect of the synchronization, with the aid of some tools for “bulk” tasks. It is certainly possible to do all this (on the FOSS version), by editing the configuration files, but I found this GUI very useful, at least for a “newbie”.

The “Data Warehousing” “pre-cooked” configuration generates a series of node groups:

  • regsrvr: registration server
  • target: target data source
  • source1: group of nodes that provide data to the target
  • source2: group of nodes that provide data to the target
  • sourceN: …

In my scenario I “left” only three nodes: the registration server, a target and a source (“source1”), and removed the other ones. The names are not so important, and I could have just called them “regsvr”, “node1” and “node2” (for instance).


The “group links” section, actually establishes the dynamics between all these groups of nodes, whatever name you called them. In my case, the registration server “waits for pulls” from both node groups (“target” and “node1”). The “target” group pushes changes to both, to the registration server and the “source1” groups. The “source1” group, pushes changes to both, the registration server and the “target” groups.


The system could be described, by something like this:


On the “routers” tab, you can define the details of these connections between nodes, through triggers (one for each action):


The triggers for each table, are defined on the “table triggers” tab.


you may defined them individually for the tables you are interested in, or do a “bulk” define by choosing “auto-create” Then, you have the option to connect the routers to the triggers on this tab, or in the “routers” tab.
When this is done, you should have a trigger for each each table, on each update/delete/remove action (according to what you have defined).

The server setup, is actually the most complex and time consuming configuration step (which I did not cover exhaustively!). After this, I went to each of my clients, and run the installation and setup again.
This time, I choose to add a “client” node instead. The “client” nodes will attempt to register during the setup, by contacting the server on the address you provide; in my case:


Unless you open the registration on the server for that particular node (by imputing its ID and group) the registration will fail. This is ok, and you can go through the entire process of creating the client, without registering the node.
When you finished the registration, if you go to the server console, and open “Manage nodes”, you will see one url under the server entry. This should be the client node, that contacted the server in order to register. If you right-click this entry, and choose “allow”, the server should be able to register the node. If you want, you may re-load the data on the client, by choosing “Send initial load to” (this actually should not be necessary, as the server should send an initial load, when allowing the node).
After registering both nodes, my setup looked like this:


After successfully registering all clients on the server, the system should be up and running. Note that you should have the symetricDS daemons running on the three nodes, to have a fully functional scenario.
I edited a record on the server, and it got replicated to the Ubuntu and Windows clients.




Then I tried to edit a record on each one of the other nodes (“target” and “client1”), and watched the changes being pushed to the other nodes. It seems that the daemon is listening for changes at very small intervals, since the changes were propagated through the system almost immediately. However I did not test it with more complex changes, including batches of data.

From this experience I would say SymmetriCDS performs quite well, and with the aid of the GUI on “symmetricDS pro”, it is not too hard to setup, once you are clear about what you are looking for and understand where to setup things. This is good because I did not find much documentation on the web apart from the simpler scenario (“Standard 2 Tier Configuration”), neither did I find posts on forums discussing this.

Furthermore it would be interesting to test this system with a “tougher” scenario: larger and more complex batches of changes, more nodes, and sometimes some (or all of) them offline. This would obviously trigger the “conflict” situation, which is also the one that “scares” me most.


Quick guide to Auditing a (postgreSQL) Database: putting it all together

On my previous post, I suggested how to create a schema, a table and a trigger function, in order to audit a PostgreSQL database.
To audit a table, you would have to create a trigger for that table, calling the code from the generic trigger.
In my case, I want to audit every table in the database, and I think most people will likely want to audit every table, or at least most tables in the database.
To escape the tedious task of writing code to implement that n-times, I put together a script that will generate an audit trigger for each table in the database.If you want to apply it to a restricted number of tables instead, you could easily change it to read the table names from a list.

CREATE OR REPLACE FUNCTION create_audit_triggers()
 _string varchar ( 1000 );	

FOR r IN SELECT distinct tablename FROM pg_catalog.pg_tables where schemaname='public'  LOOP

			     FROM information_schema.triggers
			     WHERE event_object_table = r.tablename
			     AND trigger_name = r.tablename || '_audit'

				--raise info '%' , r.tablename;
				_string :=' CREATE TRIGGER ' || r.tablename || '_audit ' ||
				' AFTER INSERT OR UPDATE OR DELETE ON ' || r.tablename ||
				' FOR EACH ROW EXECUTE PROCEDURE audit.if_modified_func();';
				raise info '%', _string; 
				EXECUTE ( _string ) ; 	

	END IF ; 

end loop;

  COST 100;
ALTER FUNCTION update_info_tables2()
  OWNER TO postgres;

This will check if the trigger already exists (for which an error would be raised!), and generate the triggers during the blink of an eye (depending on the size of your database!). Thus you could use it for updating the triggers, after you added a couple of tables in the database.

“Obscure” Databases in Detail

Carrying on a bit more detailed analysis from my previous post, I will try to point some flaws that I saw in databases stored within a Relational Database Management System (RDBMS), and discuss some solutions.

To start with the basics, why using a relational model at all to represent the data?

Skipping the obvious answer that the database is stored already using a relational engine, “normalization” (Codd, 1970) is the adoption of “normal forms of relations” with the goal of avoiding certain data anomalies that can occur in unnormalized tables. In other words, being very strict with the way of representing data is going to protect the consistency and quality of the dataset we are building. On the other hand, not being strict with the data model (or not enforcing a data model at all) may create a scenario with poor quality data, where we cannot decode the information we have, which technically means it is “lost” (even if it is physically stored in the database). Think about this as an unbreakable safe box, for which you have lost the only existing key in the entire world.

The normal forms are an attempt to make sure that you do not destroy true data or create false data in your database, so it is always a good idea to go through them when designing (or redesigning) a schema.

1. Representation of arrays

One of the “bottlenecks” in the relational model, specially for people that come from a programming background is the representation of lists or groups. Sometimes people fake the representation of arrays, by repeating a group of columns, or “flattening out” the array. Apart from the data storage overhead, this is not conform with the NF1 principle. Let us look at an example.

Table “Students”

dprt student
Geography John
Geography Martin
Geography Maria

In this example, we cannot remove a course (or update it) without involving a series of rows.

Sometimes arrays are also represented by encoding a list inside a text field, for instance separated by commas. This is also not conform with normalization, since this data type is non-existent and it is breaking the principle that the combination of row, column in a table should always refer to one record. The result is a lot of string-handling procedures to work around this kludge.

Table “Students”

dprt students
Geography John, Martin, Maria

In this example, to delete (or update) a student name we need to parse and modify the students string which is so inconvenient that I don’t need to explain further why it is not a good idea to have this approach.

The proper way to represent arrays in this case is to split the students info in two tables, and relate them with a key.

Table “Departments”

id name
1 Geography

Table “Students”

id id_dprt name
1 1 John
2 1 Martin
3 1 Maria

The two tables above are in the normalized form, and we can operate over them using the relational model. Generally, when things involve complicated procedures to work, it means they are prone to errors, so we should keep everything as simple as possible. With the table structure above, I can implement a cascade delete that will remove all the students in the Geography Department, once I close this department. This goes the same as saying, that once I close the Department, there will be no students enroled in this department lying around the database (I call them “ghost” records).

2. Use of Natural Keys

Natural keys in themselves are not discouraged. They have a meaning to the user, which is generally a good idea, and their use prevents generating extra columns on purpose to be keys. Automatic keys (like identity fields) are generally not conform with the normalized model since they bring some dependence from the physical model (and may be implemented differently by different vendors) and they bring replication issues. With the computer power that we have nowadays it is also not a such problem to have a key involving multiple columns.

However these natural keys may be “hiding” other problems, that are themselves a “threat” to a normalized database.

2.1 Are these keys “preventing” implementing the relation between tables?

In my University database, I can represent the entities: universities, departments, courses and students as different tables. Since I know, there are one-to-many relationships in Universities->Departments, Departments->Courses and Courses->Students, I can enforce this idea by using foreign keys constraints.

CREATE TABLE Universities (
    id        integer,
    name       varchar(40),

CREATE TABLE Departments (
    id        integer,
    id_universities integer references Universities(id),
    name       varchar(40),

    id        integer,
    id_dprt integer references Departments(id),
    name       varchar(40),
    CONSTRAINT pk_courses PRIMARY KEY(id)

    id        integer,
    id_courses integer references Courses(id),
    name       varchar(40),
    CONSTRAINT pk_students PRIMARY KEY(id)

If I want to know the University of a student named ‘Martin’, I just have to build my query knowing the “path” from Student to University.

Select Universities.name, Students.name
from Universities inner join Departments ON Universities.id=Departments.id_university
inner join Courses ON Departments.id=Courses.id_dprt
inner join Students ON Courses.id=Students.id_course
WHERE student.name like 'Martin'

(DISCLAIMER: please forgive me if there is some error in this query: I did not actually test it, but I think this is sufficient to get the general idea…)

On the other hand, if I don’t implement this structure in the database, and I still want to know the University of a named Student, or the University of a named course, I can do something like this.

CREATE TABLE Universities (
    name       varchar(40),
    CONSTRAINT pk_univ PRIMARY KEY(name)

CREATE TABLE Departments (
    university_name varchar(40),
    name       varchar(40),
    CONSTRAINT pk_dprt PRIMARY KEY(name)

    university_name varchar(40),
    name       varchar(40),
    CONSTRAINT pk_course PRIMARY KEY(name)

    university_name varchar(40),
    name       varchar(40),
    CONSTRAINT pk_students PRIMARY KEY(name)

In this case, I can know the University of any student (or any course), by querying a single table. The “natural key”: university_name, will provide the answer to my question. This design may run into problems, for instance if I have two students with the same name, and on the same University, but enrolled on different departments. That problem could be “solved” by bloating the Students table with a two-column key:

    university_name varchar(40),
    course_name varchar(40),
    name       varchar(40),
    CONSTRAINT pk_students PRIMARY KEY(university_name,course_name)

It is easy to imagine other violations of uniqueness or other requests of information, that could lead us to add more columns to the primary key. My whole point here is that this is a strategy to avoid implementing a relational structure in the database, and therefore it is conceptually wrong. If we know the relations between entities, we should implement them exactly as they are, and not approach the database as a set of self contained-tables.

Actually this behaviour can be tracked to people who first worked with file systems, using tapes. They tend to design one huge file and do all the work against these records; this made sense at the time, since there was no reasonable way of joining a number of small files together without having the computer mount and dismount lots of tapes. With RDBMS there is absolutely no reason to avoid joins between tables. A normalized database tends to have a lot of tables with a small number of columns per table.
If we cannot produce this sort of output because we don’t know the relations between the entities we are modelling, than I would actually question the use of a database at all (or at least, a relational database).

2.2 Do we have information represented in more than one place?

In the example above, we have the natural key “university_name” repeated all over the database. Note that there are no foreign keys, and therefore there is no guarantee that the university names on tables “Courses” and “Students” match the names on table “University”. It is easy to imagine why we may run into problems with this approach. The key that determines one attribute should be in only one table and therefore the attribute should be with it. All the other tables that “mention” this attribute should refer to this table.
Apart from the fact that we are not “repeating” information all over the database and “bloating” the storage, this is the only way of guaranteeing consistency among the dataset. If a fact appears more than once, one of the instances is likely to be in error.

The database is only good in “safeguarding” things that we implement. Since the table “Students” is not formally connected to table Courses, nothing prevents me from having a course listed in students, that is not listed at all in table “Courses”. This may be, either because I did not introduce it there, or because I misspelled it (or some other reason that I can’t remember right now). If we use an application to interface with the database, we could implement strategies to prevent this kind of errors, but actually:
why should we put this responsibility in the “shoulders” of the front-end when it could be enforced by design, in the first place?

3. Table and Column Names

Although there are not strict conventions for naming tables and columns, we should stick to the idea that tables represent either “entities” or “relationships” and the columns represent attributes, and named them accordingly. Since they store sets of rows, it is expressive if the name is in plural (like “Students” or Universities”). Actually this may seem like being over-careful, but there are not many things as bad in terms of data loss, as having a database where we don’t know what the tables or the attributes represent (and not having any enforced constraints only makes it worst).
It is also a good idea to keep the names consistent across the namespace. For instance if we have a column named “temperature” and another one “called “watertemp” and they both refer to the same thing (temperature of the water), it is generally a good idea to use the same name. This, of course, goes against the paradigm of a database as a repository of data from different sources where we don’t even change the field names when importing. But with that kind of database, there is very little you can do without a tremendous amount of work (and possible even then…).
Regarding naming conventions, everything that makes the database easier to understand and to work with, should be implemented.

4. Use of Nulls

The use of nulls brings a series of issues and should be avoided as much as possible, for instance by providing a default value or a meaningful code (“missing”, “invalid”, etc). Having many nulls on a table, may be a sign that it has not been “normalized” properly (for instance the “flattening” of an array). Nulls should *only* be used for a value that it is missing now, but may be resolved later.

5. Information that should not be stored in the database

My final thoughts go to the “temptation” of mixing the contents of the database with the mechanisms to manage this information (for instance the front-end that we use to visualize it and populate it). A database models a certain reality (for instance a University, or an Address Book). Introducing “auxiliary” tables that are tied to the front-end is possible, but not without “polluting” the dataset with information about how we deal with the dataset, which is not really part of the dataset itself. Frontends may change, and ways of visualizing data may change, but our data layer should be kept conveniently “abstracted” from all these things.
This is a reflexion that I make, that may justify the use of n-databases instead of one, where we can relate them to each other. The extra complexity required by the connection mechanism can be justified by the clarity of the system, and the simplicity of dealing only with the parts that we need.