Tracking poor queries

Tracking poor queries is an infinity quest for developers, who works with databases. Often we think that it is guilty the slow and big queries. But what to do if we do not have that kind of queries between backend and database? Which kind of queries should we looking for? What tools should be used for that work? This talk will cover all these questions.

Non-Relational Postgres

Postgres has always had strong support for relational storage. However, there are many cases where relational storage is either inefficient or overly restrictive. This talk shows the many ways that Postgres has expanded to support non-relational storage, specifically the ability to store and index multiple values, even unrelated ones, in a single database field. Such storage allows for greater efficiency and access simplicity, and can also avoid the negatives of entity-attribute-value (eav) storage. The talk will cover many examples of multiple-value-per-field storage, including arrays, range types, geometry, full text search, xml, json, and records.

Hardware acceleration options for Postgres: Intel Optane DC Persistent Memory and FPGA.

If you care about Postgres performance, there are a number of hardware acceleration options to help with different use cases. Intel Optane DC persistent memory creates new tier in data hierarchy allowing developers to utilize performance of traditional memory combining with volume and persistency of block storage devices. Unlike traditional DRAM-only in-memory systems, where memory is small, expensive, and volatile, Intel Optane DC persistent memory makes it possible to run larger Postgres databases (terabytes) in memory for higher performance. FPGAs are integrated circuits that can be reprogrammed dynamically to accelerate a specific workload such as SQL execution and data compression. FPGA accelerators extend Postgres with hundreds of SQL reader and writer processes that work in parallel on the FPGA. It’s similar to adding hundreds new cores to boost parallel processing on your server.

Migration from Oracle to PostgreSQL using an automatic converter

In the report, we want to talk about the experience of migrating one large system from Oracle to PostgreSQL. The system itself was built on the PHP + Oracle stack; its distinguishing feature was that all business logic was implemented in PL / SQL code. In a DBMS, there are more than 3000 packages with 4-10 functions (procedures) in each. In PHP - more than 10,000 forms with inserts of anonymous blocks used to receive data, process and save results in Oracle.

To solve this extremely voluminous work, we took ANTLR4 (a powerful parser generator for reading, processing, executing, or translating structured text), PL/SQL grammar, and created an automatic converter that allows you to convert all objects in the schema and our system from Oracle to working code for PostgreSQL.