We explore Cloud SQL PostgreSQL 13. A fully-managed instance of the world’s most advanced open-source relational database
PostgreSQL is a powerful, open-source object-relational database system. It shares many commonalities with other relational database platforms, including Microsoft SQL Server and MySQL.
Part of the Google Cloud Platform ecosystem, Cloud SQL is a fully-managed database service designed to make it easy to set up, manage, and administer relational databases on Google Cloud Platform. At the time of writing, Cloud SQL supports MySQL, PostgreSQL, and SQL Server.
PostgreSQL started out in life in 1986 as a military-sponsored project, codenamed POSTGRES.
Developed at the University of California at Berkeley, the project received significant backing from Defense Advanced Research Projects Agency (DARPA), the Army Research Office (ARO), the National Science Foundation (NSF), and ESL, Inc. Excerpt from A Brief History of PostgreSQL
This much-anticipated release appears to be heavily centered around scalability, helping the platform to cope with ever-increasing storage volumes.
PostgreSQL 13 includes significant improvements to its indexing and lookup system that benefit large databases, including space savings and performance gains for indexes, faster response times for queries that use aggregates or partitions, better query planning when using enhanced statistics, and more. PostgreSQL 13 release note
You can really tell from this, that the engineers at PostgreSQL are going after addressing some of the challenges with query performance and storage size that users with large databases will be facing. And it’s good timing too, as we see more and more clients managing databases that now fall into this “large” category as they ingest more and more data.
Building on work from the previous PostgreSQL release, PostgreSQL 13 can efficiently handle duplicate data in B-tree indexes, the standard database index. This lowers the overall space usage that B-tree indexes require while improving overall query performance. PostgreSQL 13 release note
We think this will benefit many users of the platform, as B-tree index duplicates are very common in any relational database. Duplicate data occurs when multiple leaf-nodes in the index (leaf-nodes point to a physical row in a table) contain the same value as another leaf-node and both point to the same physical row.
Duplicates include NULL values, so if you have an index that covers a sparsely populated column, you should see some good benefits from this change.
Deduplication is enabled by default but can be disabled. We note the process is managed lazily, and therefore should not impact DML performance.
PostgreSQL 13 introduces incremental sorting, where sorted data from an earlier step in a query can accelerate sorting at a later step. Additionally, PostgreSQL can now use the extended statistics system (accessed via CREATE STATISTICS) to create improved plans for queries with OR clauses and IN/ANY lookups over lists. PostgreSQL 13 release note
This is going to be a useful tool to have in the box for solving individual queries that are running slowly. Historically, database developers were very much at the mercy of the PostgreSQL optimiser; whilst it’s easy to view execution plans, it was often difficult to prevent the optimiser from making a “mistake” (for example, in underestimating the number of rows returned from an operation). And these can lead to slow running queries that are difficult to tune.
Extended statistics allow database developers and administrators to provide their own statistics, to complement the statistics built offline and served to the optimiser. For example, you can indicate which columns in a table are highly correlated (that is, generally have a 1–1 mapping, such as an employee id and their employee title id).
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SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.
Models for SQL exist. In any case, the SQL that can be utilized on every last one of the major RDBMS today is in various flavors. This is because of two reasons:
1. The SQL order standard is genuinely intricate, and it isn’t handy to actualize the whole standard.
2. Every database seller needs an approach to separate its item from others.
Right now, contrasts are noted where fitting.
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A multi-cloud approach is nothing but leveraging two or more cloud platforms for meeting the various business requirements of an enterprise. The multi-cloud IT environment incorporates different clouds from multiple vendors and negates the dependence on a single public cloud service provider. Thus enterprises can choose specific services from multiple public clouds and reap the benefits of each.
Given its affordability and agility, most enterprises opt for a multi-cloud approach in cloud computing now. A 2018 survey on the public cloud services market points out that 81% of the respondents use services from two or more providers. Subsequently, the cloud computing services market has reported incredible growth in recent times. The worldwide public cloud services market is all set to reach $500 billion in the next four years, according to IDC.
By choosing multi-cloud solutions strategically, enterprises can optimize the benefits of cloud computing and aim for some key competitive advantages. They can avoid the lengthy and cumbersome processes involved in buying, installing and testing high-priced systems. The IaaS and PaaS solutions have become a windfall for the enterprise’s budget as it does not incur huge up-front capital expenditure.
However, cost optimization is still a challenge while facilitating a multi-cloud environment and a large number of enterprises end up overpaying with or without realizing it. The below-mentioned tips would help you ensure the money is spent wisely on cloud computing services.
Most organizations tend to get wrong with simple things which turn out to be the root cause for needless spending and resource wastage. The first step to cost optimization in your cloud strategy is to identify underutilized resources that you have been paying for.
Enterprises often continue to pay for resources that have been purchased earlier but are no longer useful. Identifying such unused and unattached resources and deactivating it on a regular basis brings you one step closer to cost optimization. If needed, you can deploy automated cloud management tools that are largely helpful in providing the analytics needed to optimize the cloud spending and cut costs on an ongoing basis.
Another key cost optimization strategy is to identify the idle computing instances and consolidate them into fewer instances. An idle computing instance may require a CPU utilization level of 1-5%, but you may be billed by the service provider for 100% for the same instance.
Every enterprise will have such non-production instances that constitute unnecessary storage space and lead to overpaying. Re-evaluating your resource allocations regularly and removing unnecessary storage may help you save money significantly. Resource allocation is not only a matter of CPU and memory but also it is linked to the storage, network, and various other factors.
The key to efficient cost reduction in cloud computing technology lies in proactive monitoring. A comprehensive view of the cloud usage helps enterprises to monitor and minimize unnecessary spending. You can make use of various mechanisms for monitoring computing demand.
For instance, you can use a heatmap to understand the highs and lows in computing visually. This heat map indicates the start and stop times which in turn lead to reduced costs. You can also deploy automated tools that help organizations to schedule instances to start and stop. By following a heatmap, you can understand whether it is safe to shut down servers on holidays or weekends.
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If you looking to learn about Google Cloud in depth or in general with or without any prior knowledge in cloud computing, then you should definitely check this quest out, Link.
Google Could Essentials is an introductory level Quest which is useful to learn about the basic fundamentals of Google Cloud. From writing Cloud Shell commands and deploying my first virtual machine, to running applications on Kubernetes Engine or with load balancing, Google Cloud Essentials is a prime introduction to the platform’s basic features.
Let’s see what was the Quest Outline:
A Tour of Qwiklabs and Google Cloud was the first hands-on lab which basically gives an overview about Google Cloud. There were few questions to answers that will check your understanding about the topic and the rest was about accessing Google cloud console, projects in cloud console, roles and permissions, Cloud Shell and so on.
**Creating a Virtual Machine **was the second lab to create virtual machine and also connect NGINX web server to it. Compute Engine lets one create virtual machine whose resources live in certain regions or zones. NGINX web server is used as load balancer. The job of a load balancer is to distribute workloads across multiple computing resources. Creating these two along with a question would mark the end of the second lab.
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I’ve found that the strength of Google Cloud’s services comes from consistently delivering across a few key metrics, namely reliability and performance. When dealing with SQL databases, it’s hard to imagine any metrics more important than these two things, and Google’s Cloud SQL delivers.
To become familiar with Cloud SQL, we’re going to walk through the creation of a SQL database and explore the advantages Google Cloud offers us when compared to other solutions. We’ll also be dipping into the Cloud SQL API to see how we might manage our SQL database programmatically.
There aren’t many metrics to consider when comparing Cloud database instances aside from price and performance. I’ve found Cloud SQL to come in on the higher end of both these metrics.
Google Cloud’s databases tend to clock in at the higher end of performance benchmarks. To demonstrate, I’m going to borrow a quick comparison of GCP vs AWS cloud databases:
When compared to AWS RDS (green), GCP SQL instances (purple) clock in at better performance speeds across the board. There are other metrics we could compare, but chances are you’re going to go with the database of whichever cloud vendor you’re locked in with anyway.
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Slow systems are a bane of any product. Ask the audience of web applications, and you would know. Research has found that 47% of users expect web pages to be loaded in 2 seconds or less, while 40% of users would abandon a site if it takes more than 3 seconds to load¹!
While there are numerous methods to optimize the performance of a web application, we want to focus on the performance of our backend for this discussion. From the backend, the performance of our database queries is “front and center” to the latency of our APIs, which impacts our frontend application’s user experience directly.
And if there are inefficient queries that take a long time to resolve (or timeout), it will be a sore spot that your users will directly experience (and liking be complaining about!).
In the following, I want to touch on a few methodologies and tools that we can easily use to measure and optimize queries in PostgreSQL. I’ll go on further to talk about Cloud SQL’s latest tool, Query Insights, something I am really excited to share for this same topic.
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