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Postgres And Transparent Data Encryption (TDE)

Security has always been a great concern of Enterprises. Especially, if you have crucial information stored in the database, you would always prefer to have high security around it. Over the years, technologies have evolved and provided better solutions around it. If you have very sensitive information, people try to keep this information encrypted so, that in case, somebody gets access of the system, then they cannot view this information, if they are not authorized. For managing sensitive information, Enterprises use multiple methods: 1. Encrypting specific information. If you are PPAS users, you would like to use DBMS_CRYPTO package which provides a way of encrypting sensitive information in databases. For more information, please refer following link: http://www.enterprisedb.com/docs/en/9.4/oracompat/Database_Compatibility_for_Oracle_Developers_Guide.1.178.html# For PostgreSQL, users can use pgcrypto module. 2. Transparent Data Encryption (TDE) is anot

Dynamic RLS implementation in PPAS 9.3

In the course of my work at EnterpriseDB, migrating Oracle databases to EnterpriseDB's Postgres Plus Advanced Server is a common task. However, now and then we encounter unique situations. While working on a migration project recently, we encountered a new use case for RLS (Row level Security). The customer had a centralized database where it stored a huge number of transactions. These transactions are performed by different business units located in different parts of the world. There are certain types of transactions that should not be visible even if they are being queried by the same company. That is where RLS comes in. With RSL, specific transactions, or kinds of transactions, that can remain visible are mapped back to an attribute in the table. The customer needed the application to authenticate users and set the context for which records in the database become visible for a specific session. In its deployment of Oracle, the customer had used the functions/procedure in t

Compiling PLV8 with Postgres Plus Advanced Server

PLV8 is a programming language that lets users write stored procedures and triggers in JavaScript and store them in their Postgres database. This allows application programmers to write a lot of their server-side programming in the same language they use to build their web client applications.  Fewer languages to learn usually means fewer mistakes and faster time to completion.  The extensive language support is one of many reasons why Postgres’ use across the world is increasing lately.  The recent addition of document data support with JSON and JSONB data types in PostgreSQL, and in Postgres Plus Advanced Server from EnterpriseDB, is the main reason for the increasing interest in the PL/V8 language extension. Below are the steps you need to compile PLV8 with Postgres Plus Advanced Server 9.3/9.4. To get started, here are the prerequisites: 1. A supported version of PostgreSQL or Postgres Plus Advanced Server, such as versions 9.1 and higher. 2. V8 version 3.14.5 3. g++ version

Meet BART – A New Tool for Backup And Recovery Management

EnterpriseDB recently launched a new tool for backup and recovery – named simply EDB Backup and Recovery Tool, or  BART . This tool makes the DBA’s life easier by simplifying the tasks for managing their Postgres physical backup and recovery tasks, whether they are PostgreSQL or Postgres Plus Advanced Server deployments. BART has the following advantages over custom scripts for managing backups: 1.  It’s stable and it uses the tool pg_basebackup to take a physical backup. This tool has been well defined and is well-supported by the PostgreSQL community. 2.  It catalogs all of the backups users are taking, which is important in terms of:     i.  Listing the type of backups used    ii.  Listing the status of those backups with server information. 3.  BART also provides functionality to restore backups, with all required archived WAL files. So automation around this tool will make DBAs’ lives easier for restore and recovery. 4.  BART provides an option to validate your backup

Switchover/Switchback in PostgreSQL 9.3

PostgreSQL 9.3 has two key software updates making switchover/switchback easier in High Availability configurations. First, let’s address the software patches and their descriptions: 1. First patch was committed by Fujii Masao. Patch commit#  985bd7d49726c9f178558491d31a570d47340459 With this patch, the walsender process tries to send all outstanding WAL records to the standby in replication when the user shuts down the master. This means:  a. All WAL records are synced between two servers after the clean shutdown of the master  b. After promoting the standby to new master, the user can restart the stopped master as new standby without a fresh backup from new master. 2. Second patch was committed by Heikki Linnakangas in PostgreSQL 9.3. Patch commit# abfd192b1b5ba5216ac4b1f31dcd553106304b19 Before PostgreSQL version 9.3, streaming replication used to stop replicating if the timeline on the primary didn’t match the standby. This generally happens when the user promotes

Monitoring approach for Streaming Replication with Hot Standby in PostgreSQL 9.3.

The people using PostgreSQL and the Streaming Replication feature seem to ask many of the same questions: 1. How best to monitor Streaming Replication? 2. What is the best way to do that? 3. Are there alternatives, when monitoring on Standby, to using the pg_stat_replication view on Master? 4. How should I calculate replication lag-time, in seconds, minutes, etc.? In light of these commonly asked questions, I thought a blog would help. The following are some methods I’ve found to be useful. Monitoring is critical for large infrastructure deployments where you have Streaming Replication for: 1. Disaster recovery 2. Streaming Replication is for High Availability 3. Load balancing, when using Streaming Replication with Hot Standby PostgreSQL has some building blocks for replication monitoring, and the following are some important functions and views which can be use for monitoring the replication: 1. pg_stat_replication view on master/primary server.    This view helps in

Write Operation: MongoDB Vs PostgreSQL 9.3 (JSON)

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PostgreSQL 9.3  has lot of new improvement like the addition of new operators for JSON data type in postgreSQL, that prompted me to explore its features for NoSQL capabilities. MongoDB is one of NoSQL solutions that have gotten a great deal of attention in the NoSQL market. So, this time I thought to do some benchmarking with the NoSQL capability of JSON in MongoDB and the JSON datatype in PostgreSQL 9.3 For this benchmark, I have used the same machine with no optimization in installation of PostgreSQL and MongoDB (since I wanted to see how things work, out of box with default installation). And I used the sample data from MongoDB's site, around which I had developed the functions which can generate random data using the same sample for Mongo and for PostgreSQL. In this benchmarking, I have verified following: 1. PostgreSQL COPY Vs Mongo-Import 2. Data Disk Size of PostgreSQL and Mongo for same amount of data. 3. PostgreSQL INSERT Vs Mongo Insert Some specification befor