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Do you know that story about the Tooth Fairy? or the other one that you heard that a friend of a friend of an acquaintance of the nephew's aunt's brother has used and is it cool?
So, my pleasure is Babelfish, it's ugly, it has some problems, it looks like a politician (it sells more than it delivers), but it's here to help you.
But what is it for?
Imagine the following, by some fate of fate, people discovered that licensing SQL Server is expensive, you can't keep using SQL Enterprise to maintain the company's foundations, the Standard is not an option (because after all, it's the Std and nobody likes it, just kidding) for whatever reason, sometimes even a new manager's tantrum with SQL Server (he doesn't know how to use it and wants to give a hint of a DBA), cut costs, choose the reason, whatever, it's just an excuse for: why not? ¨
As a DBA, we have to keep an open mind to other headaches, after all, our life is quiet and quiet so as not to suffer from multi-platform or multi-bank.
THEORETICALLY, this guy should help you achieve a smoother transition between SQL Server and Postgresql, as an intermediary for interpreting T-SQL to PLPGSQL at run time.
To fully understand how AWS Babelfish works, it's important to dive into the technical and communication layers that facilitate compatibility between SQL Server and PostgreSQL. Babelfish acts as a bridge between these two worlds, interpreting SQL Server calls to the PostgreSQL format and vice versa, allowing applications written for SQL Server to interact with Aurora PostgreSQL as if they were communicating with a native SQL Server database. Let's detail those layers and the communication process.
Understanding the Layers of Babelfish
Babelfish implements two main layers of functionality:
Communication Protocol Layer:
SQL Server uses the TDS (Tabular Data Stream) protocol for communication between client and server. Babelfish is able to understand and interpret the TDS protocol, allowing SQL Server clients to connect to Aurora PostgreSQL as if it were an SQL Server server. This communication layer translates TDS requests into SQL calls understandable by PostgreSQL.
SQL Translation Layer:
After communication is established through TDS, SQL statements, stored procedures, functions, and data types specific to SQL Server are translated into their equivalents in PostgreSQL. This layer deals with the conversion of query syntaxes, data types, and execution semantics, ensuring that the operations performed by the applications work correctly in Aurora PostgreSQL.
How Babelfish Works in Practice
The interaction process between an SQL Server application and Aurora PostgreSQL through Babelfish involves several steps:
Connection Establishment:
The SQL Server client initiates a connection using the TDS protocol, directed to the Babelfish endpoint on the Aurora PostgreSQL instance.
Babelfish accepts the TDS connection, establishing a communication channel between the client and Aurora PostgreSQL.
Executing Queries:
When a SQL Server query is sent by the client, the Babelfish communication protocol layer receives the request in TDS format.
The SQL translation layer then converts the query from the SQL Server format to a PostgreSQL-compatible query, including the translation of data types and the syntax of stored procedures, if necessary.
Query Processing:
The translated query is processed by Aurora PostgreSQL, and the results are generated.
The results are then encapsulated in a format understandable by the TDS protocol and sent back to the SQL Server client through Babelfish.
Return of Results:
The SQL Server client receives the results as if it were interacting with a native SQL Server database, completing the operation.
Implementation Considerations
Compatibility: While Babelfish offers a high degree of compatibility, there are limitations and differences that must be considered. Not all SQL Server features and behaviors are supported 1:1 in PostgreSQL through Babelfish.
Performance Optimization: Translating between SQL dialects can introduce performance overheads. It is important to monitor the performance of the applications and adjust the Babelfish queries or configuration as necessary.
Transaction Management: Babelfish supports transactions, but the differences in transaction management between SQL Server and PostgreSQL may require special attention to ensure data integrity.
Installing and Configuring Babelfish Locally
Prerequisites
Before we begin, make sure that you have the following prerequisites in your system:
Compatible operating system (Linux/Windows)
PostgreSQL up and running
Administrative access to the system
Basic knowledge of command line and database administration
Step 1: Download the Babelfish Binary
Initially, you need to get the Babelfish binary for your platform. AWS makes these binaries available through its official website or GitHub repository. Visit the Babelfish GitHub to find the latest version compatible with your system.
Step 2: Babelfish Installation
After downloading, follow the steps specific to your platform to unzip and install the binary. On Linux-based systems, it usually involves extracting the contents of the file and running an installation script. For example:
tar -zxvf babelfish-version-linux.tar.gz cd babelfish-version. /install.sh
Step 3: Configuring PostgreSQL for Babelfish
With Babelfish installed, the next step is to configure your PostgreSQL to work with Babelfish. This generally involves editing the PostgreSQL postgresql.conf configuration file to include Babelfish-specific parameters, such as:
# Example of parameters to be added to postgresql.conf listen_addresses = '*' babelfishpg_tsql.database_name = 'SQLServer BankName'
Don't forget to restart the PostgreSQL service after making these changes.
Step 4: Creating a SQL Server-Compliant Database
You will need to create a PostgreSQL database that Babelfish will use to emulate SQL Server behavior. This can be done using psql, PostgreSQL's interactive terminal, with commands such as:
CREATE DATABASE SQL Server database name;
Step 5: Testing the Connection
After setup, it's crucial to test the connection to ensure that everything is working as expected. Use your usual SQL Server tools to connect to the configured PostgreSQL database, using the IP address and port where PostgreSQL is running.
If everything works out, you'll be inside the SSMS running a T-SQL query against a Postgres database.
Installing and Configuring Babelfish on AWS
The installation of Babelfish is performed by creating an instance of Amazon Aurora PostgreSQL that includes the Babelfish compatibility layer. See the steps:
Create an instance of Amazon Aurora PostgreSQL:
Go to the AWS console and select the Amazon RDS service.
Click “Create Database” and select “Amazon Aurora”.
Choose the PostgreSQL-compatible edition and the version that includes Babelfish.
Configure the instance specifications as necessary and proceed with the creation.
Enable Babelfish on the Instance:
After creating the instance, navigate to the parameters section and enable Babelfish by adding the appropriate configuration. This may require creating a new parameter group if you want specific customizations.
Babelfish Setup
After installation, you need to configure Babelfish to accept SQL Server connections:
Configure the SQL Server Endpoint:
Locate the Babelfish endpoint in the connectivity section of the Aurora PostgreSQL instance.
This endpoint is used to connect SQL Server applications to Babelfish.
Parameter Adjustment:
Access the instance parameter group and adjust settings to optimize performance and compatibility with your SQL Server applications.
Points of Attention
It may seem like the infallible solution to all your problems, but since there is no such thing as a free lunch, it has limitations:
Officially supported version of Postgres, on local installation the version so far is 14.6
Limitations of “translations”, although he performs some miracles in his grotesque code, he has certain limitations, take a look at the Bablefish website to see what he can do.
A resource consumer, it is already to be expected that since he is going to do a simultaneous translation of commands directly on the channel, the greater the computing power the faster he will translate the commands, so I see this as one of the main bottlenecks of adoption in technology, since hardware is money and this can be expensive.
