In the previous article, we’ve looked into various ways to store the date and time with PostgreSQL and TypeORM. Postgres can also manage intervals. With them, we can store a period of time.
There are various ways we can input and view interval values. By default, PostgresSQL represents intervals using a format called postgres. We can check it by viewing the IntervalStyle parameter.
It is essential to keep the correct time on a server. This is especially true when it comes to processing financial transactions or other vital functions which need to be handled in a specific order. Using the Network Time Protocol (or NTP), computers can synchronize their internal clock times with the internet standard reference clocks. In essence, NTP is a hierarchy of servers. The higher the Stratum number of a server, the more accurate the timing is and the lower the Stratum number of a server is, the lower the accuracy and time stability. Stratus are defined by the distance from the initial reference clock.
#tutorials #atomic clock #centos #chrony #clock #drift #internal time clock #network time protocol #ntp #offset #peers #pool.ntp.org #server time #stratum #system clock #time #time drift #timekeeping #ubuntu #utc
Dealing with dates and times in Python can be a hassle. Thankfully, there’s a built-in way of making it easier: the Python datetime module.
datetime helps us identify and process time-related elements like dates, hours, minutes, seconds, days of the week, months, years, etc. It offers various services like managing time zones and daylight savings time. It can work with timestamp data. It can extract the day of the week, day of the month, and other date and time formats from strings.
#data science tutorials #calendar #date #dates #datetime #intermediate #python #time #time series #times #tutorial #tutorials
If you did not enjoy handling dates and times before, I would recommend you going through my post and hopefully, you will change your mind. Let’s start.
If you would like to display the date, time with the timezone at the end, you can use either now() or CURRENT_TIMESTAMP as follows
If you would like to display the date, time without the timezone , you can use now()::timestamp or LOCALTIMESTAMP as follows
#postgresql #timezone #dates #times #timestamp #aclearsummary
Data management, analytics, data science, and real-time systems will converge this year enabling new automated and self-learning solutions for real-time business operations.
The global pandemic of 2020 has upended social behaviors and business operations. Working from home is the new normal for many, and technology has accelerated and opened new lines of business. Retail and travel have been hit hard, and tech-savvy companies are reinventing e-commerce and in-store channels to survive and thrive. In biotech, pharma, and healthcare, analytics command centers have become the center of operations, much like network operation centers in transport and logistics during pre-COVID times.
While data management and analytics have been critical to strategy and growth over the last decade, COVID-19 has propelled these functions into the center of business operations. Data science and analytics have become a focal point for business leaders to make critical decisions like how to adapt business in this new order of supply and demand and forecast what lies ahead.
In the next year, I anticipate a convergence of data, analytics, integration, and DevOps to create an environment for rapid development of AI-infused applications to address business challenges and opportunities. We will see a proliferation of API-led microservices developer environments for real-time data integration, and the emergence of data hubs as a bridge between at-rest and in-motion data assets, and event-enabled analytics with deeper collaboration between data scientists, DevOps, and ModelOps developers. From this, an ML engineer persona will emerge.
#analytics #artificial intelligence technologies #big data #big data analysis tools #from our experts #machine learning #real-time decisions #real-time analytics #real-time data #real-time data analytics
In our previous posts in this series, we spoke at length about using PgBouncer and Pgpool-II , the connection pool architecture and pros and cons of leveraging one for your PostgreSQL deployment. In our final post, we will put them head-to-head in a detailed feature comparison and compare the results of PgBouncer vs. Pgpool-II performance for your PostgreSQL hosting !
The bottom line – Pgpool-II is a great tool if you need load-balancing and high availability. Connection pooling is almost a bonus you get alongside. PgBouncer does only one thing, but does it really well. If the objective is to limit the number of connections and reduce resource consumption, PgBouncer wins hands down.
It is also perfectly fine to use both PgBouncer and Pgpool-II in a chain – you can have a PgBouncer to provide connection pooling, which talks to a Pgpool-II instance that provides high availability and load balancing. This gives you the best of both worlds!
PostgreSQL Connection Pooling: Part 4 – PgBouncer vs. Pgpool-II
While PgBouncer may seem to be the better option in theory, theory can often be misleading. So, we pitted the two connection poolers head-to-head, using the standard pgbench tool, to see which one provides better transactions per second throughput through a benchmark test. For good measure, we ran the same tests without a connection pooler too.
All of the PostgreSQL benchmark tests were run under the following conditions:
We ran each iteration for 5 minutes to ensure any noise averaged out. Here is how the middleware was installed:
Here are the transactions per second (TPS) results for each scenario across a range of number of clients:
#database #developer #performance #postgresql #connection control #connection pooler #connection pooler performance #connection queue #high availability #load balancing #number of connections #performance testing #pgbench #pgbouncer #pgbouncer and pgpool-ii #pgbouncer vs pgpool #pgpool-ii #pooling modes #postgresql connection pooling #postgresql limits #resource consumption #throughput benchmark #transactions per second #without pooling