In the previous part, we created our application and our 2 functions.

Now we need to create an API Gateway to be able to call these functions from outside OCI. Using a third party scheduler, our laptop, etc…

Before creating the gateway, we need to create some policy to allow the API Gateway to call our functions. I’ve tried to create the policy after, it seems to not work as expected.

Security

Dynamic Group

We need to create a Dynamic Group that will match our gateway:

The matching rule is checking the type of resource that should be ApiGateway and my compartment’s id:

All {resource.type='ApiGateway', resource.compartment.id = 'ocid1.compartment.oc1..xxxxxxxxx'}

Policy …

In my previous post, I explained how to deal with Performance_Schema and Sys to identify the candidates for Query Optimization but also to understand the workload on the database.

In this article, we will see how we can create an OCI Fn Application that will generate a slow query log from our MySQL Database Service instance and store it to Object Storage.

The creation of the function and its use is similar to the one explained in the …

MySQL is an outstanding database for online transaction processing. With suitable hardware, it is easy to execute more than 1M queries per second and handle tens of thousands of simultaneous connections. Many of the most demanding web applications on the planet are built on MySQL. With capabilities like that, why would MySQL users need anything else?  

Well, analytic queries for starters. Analytic queries answer important business questions like finding the number of unique visitors to a website over time or figuring out how to increase online purchases. They scan large volumes of data and compute aggregates, including sums, averages, and much more complex …

We are relocating MySQL Connector/J artifacts in Maven in order to make things right and follow current Central Repository publishing requirements. Please be prepared to update dependencies in your project's POM file.

Statement Digest in MySQL 

https://dev.mysql.com/doc/refman/8.0/en/performance-schema-statement-digests.html

The parser is also used by the STATEMENT_DIGEST_TEXT() and STATEMENT_DIGEST() functions, which applications can call to compute a normalized statement digest and a digest hash value, respectively, from an SQL statement.

events_statement_summary_by_digest in Performance Schema contains information

…

A few weeks back, we faced an issue in a replication environment for a Managed Services client:

LAST_ERROR_MESSAGE: Worker 2 failed executing transaction ‘UUID:GTID’ at master binlog.0012345, end_log_pos 98765; Error ‘Operation CREATE USER failed for ‘test_user’@’10.10.10.10” on query. Default database: ‘mysql’. Query: ‘CREATE USER ‘test_user’@’10.10.10.10’ IDENTIFIED WITH ‘mysql_native_password’ AS ‘************”

After some initial investigation, we noticed that the user in the replica didn’t exist! Was MySQL going crazy? But then the customer mentioned they had the following error in the primary before being able to execute the query successfully:

root@localhost [mysql]> CREATE USER 'test_user'@'10.10.10.10' identified WITH 'mysql_native_password' BY …

MySQL gave us the JSON data type back in mid-2015 with the release of MySQL 5.7.8. Since then, it has been used as a way to escape rigid column definitions and store JSON documents of all shapes and sizes: audit logs, configuration settings, 3rd party payloads, user-defined fields, and more.

Although MySQL gives us functions for reading and writing JSON data, you’ll quickly discover something that is conspicuously missing: the ability to directly index your JSON columns.

In other databases, the best way to directly index a JSON column is usually through a type of index known as a Generalized Inverted Index, or GIN for short. Since MySQL doesn’t offer GIN indexes, we’re unable to directly index an entire stored JSON document. All is not lost though, because MySQL does give us a way to indirectly index parts of our stored JSON …

Geo locations are a cornerstone of modern applications. Whether you’re a food delivery business or a family photographer, knowing the closest “something” to you or your clients can be a great feature.

In our ‘Scaling and Optimization’ training class for MySQL, one of the things we discuss is column types. The spatial types are only mentioned in passing, as less than 0.5% of MySQL users know of their existence (that’s a wild guess, with no factual basis). In this post, we briefly discuss the POINT type and how it can be used to calculate distances to the closest public park.

Import the data

To start off, we need a few tables and some data. The first table will hold the mapping between the zip code and its associated latitude/longitude. GeoNames has this data under the Creative Commons v3 license, …

Let’s continue the discovery of the MySQL Database Resource when deploying on Oracle Cloud Infrastructure using Terraform.

Last week, we saw how to create custom configurations and define user variables. Today we will see how we can define a backup policy and a maintenance window.

Backup Policy

In the oci_mysql_mysql_db_system resource, we will add a new section called backup_policy like this:

backup_policy {
       is_enabled        = "true"
       retention_in_days = "3"
       window_start_time = "01:00-00:00"
       freeform_tags = {"backup_defined_by"="Terraform"}
       pitr_policy {
            is_enabled = "true"
       }
}

This part of code (you can see in a working Terraform architecture sample), enables backup, sets the retention days to 3. It also defines the starting time …

Where I work, there is an ongoing discussion about test data generation. At the moment we do not replace or change any production data for use in the test environments, and we don’t generate test data.

That is safe and legal, because production data is tokenized. That is, PII and PCI data is being replaced by placeholder tokens which can be used by applications to access the actual protected data through specially protected access services. Only a very limited circle of people is dealing with data in behind the protected services.

Using production data in test databases is also fast, because we copy data in parallel, at line speed, or we make redirect-on-write (“copy-on-write” in the age of SSD) writeable snapshots available.

Assume for a moment we want to change that and

• mask data from production when we copy it to test databases
• reduce the amount of data used in test databases, while …