PgConf.Russia 2019

This report focuses on the continuation of transferring our ECM “Priority” from MS SQL to Postgres. Technical solutions, issues of rewriting from T-SQL to plpgsql, optimization of the effective code and moving data will be covered. Additionally, there will be considered aspects of pgplsql performance testing to find the “bad code” of pgplsql as a candidate for optimization. The main objective of the presentation is to answer the question: "We have it in T-SQL - how to transfer it in PG?". The report is intended for junior Postgres developers and is a continuation of the previous report made at the conference in 2017(https://youtu.be/v6_4Szr8t14).

So you finally have your database model for your application and you fill it in with current data. How do you keep it up to date? While INSERT may still be transparent, UPDATE and DELETE will overwrite your previous data, so you won't be able to reproduce them. Cloning the whole huge content for each minor update is not an option. For rich and complex data about hundreds of thousands of power generators in Germany and worldwide, I built a model using range data types in recent PostgreSQL which allows me to insert, update and delete data while granting the full access to the whole state of the database at any historical moment. I'll present a very simplified version of the database so the audience will be immediately able to apply it for their cases. I'll also show a few tricks in Python and Psycopg2 that will allow a whole team to prepare, review, and deploy all revisions to this database without merge conflicts. And I'll give a few ideas on how to retrieve this data efficiently.

This talk will represent a new platform of Distributed Control System for Nuclear Power Plant operation. Participants will learn about control system for very complicated automation objects. In a hard real time node more than 150 special subsystems are operating in order to control various technological processes of nuclear power plant (NPP), such as reactor control system for more than 1000 MW power unit with a turbine weighing more than 2000 tons. More than 100K of data gained from sensors are resulting in up to 500K of parameters representing 5 branches of physical processes: neutron kinetics, hydrodynamics, chemistry and radiochemistry, and physics of strength. Deviations may cause the whole system to become a huge DDoS source made of useful diagnostic information which is always much larger than the network and hardware are capable to manage. This may lead to normal operation failure. The talk will reveal the approaches to solve the issue.

You will learn about hardware and software architecture of such systems, about backup and replication, data redundancy and technological diversity. How to manage high loads, what is QoS, and what will happen in case of normal operation system failure, as for example was at Fukushima. But, hey, there should be a talk about coding! So, no SSD and HDDs, only InMemory, data structures from tens of millions of elements, and forget about processor cache as it does not work. Imagine your newest 4-generation Xeon has lost all the advantages and turned into a "pumpkin", so let's roll up your sleeves and examine timings, synchronicity, and try to make the most of your hardware, discovering the weakest link from processor, operating system and a network.

Many DBMS specialists do not like these three letters - ORM because they have repeatedly seen the enormous queries ORM-generated for simplest operations. However practice shows that the origin of the problem is not ORM itself but rather those developers who are not able to use ORM properly. In this report I will tell you the basic principles of how to write code for ORM which generates "good" queries and also show you "bad" code samples and what you get out of them. The main idea is we have to think in SQL-style when writing the code, and so to learn to foresee what kind of query will be generated. But even having mastered that you must always check the output SQL for complex queries. I will show an example when a slight change in ORM-logic increases the volume of output SQL by dozens of times(!). I will tell you about additional tools and tricks. Namely - disabling tracking, INCLUDE construction, alternative syntax for JOIN, how to get more data using a smaller number of queries, how to effectively write queries with grouping, and what do we need mappings for. I will not bypass the cases when it is not possible to effectively solve the problem by means of ORM (for example, queries with recursion). In addition to SELECT requests, there are some Batch-Update/Delete tools that allow you to update and delete data using ORM tools without downloading data to the client side. We'll also talk on how to force the ORM to insert large volumes of data quickly via Multi-Insert and COPY. I will also discuss how ORM supports PostgreSQL-specific data types i.g. arrays, hstore and jsonb. But does it make sense to use ORM at all, since there is so much to learn? Sure it does. There are advantages of using ORM, and we will discuss them as well. All examples are based on Entity Framework technology for .Net Core and .Net Framework in C#. There are some subtle differences in ORM usage in Hibernate/NHibernate, but the basic principles remain the same, so the report will be useful for developers using various technologies.