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What’s new for Google Cloud databases — December 2024

Google has announced its recognition by Forrester for its cloud databases work, acquiring the accolade of Leader in The Forrester Wave™: Translytical Data Platforms, Q4 2024 report.
Google has created a generative AI whitepaper for the data industry that describes the four pillars of successful AI database applications.
Google Cloud's databases have seen a range of product updates, including Cloud SQL's integration with Certificate Authority Service.
Advanced Disaster Recovery for PostgreSQL and MySQL has launched with preview functionality, providing seamless disaster recovery testing and execution without application changes.
Google's Cloud SQL Enterprise+ for PostgreSQL and MySQL has integrated data cache enable/disable to allow users near-zero downtime for MySQL and PostgreSQL primary instances and scalability through instance scaledown.
Memorystore for Redis Cluster has introduced single shard clusters, node-level metrics, and OSS autoscaler for optimized automatic performance, capacity, and budget optimization in customer clusters.
Google has also announced improved existing products; including enhanced availability for AlloyDB, which has extended storage and supports in-place version upgrades, and Spanner, which provides directed reads functionality and improves query efficiency with the query optimizer now in GA.
Ford Pro Intelligence, Unity Ads, Tricent Security Group, Palo Alto Networks, and Flipkart have been highlighted as businesses that are successfully transforming through Google Cloud database solutions.
Google Cloud also offered a list of upcoming events, including webinars on database solutions like CloudSQL and Memorystore in combination and a discussion with Bayer concerning geospatial data processing.
Finally, those looking to try Google Cloud databases can now benefit from a 30-day AlloyDB free trial instance, a 90-day Spanner free trial, and $300 in free credits for new Cloud SQL customers.

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Dbi-Services

357

Oracle Database Replay for a RAC environment

The database Capture/Replay feature of the Real Application Testing (RAT) suite enables you to capture the workload from a production database and replay it on a test or development environment.
In this blog, we’ll explore how to accomplish this when both your production and test databases are configured in a two-node RAC environment.
You should be aware that some workload operations are not supported, such as flashback queries and distributed transactions.
The capture procedure on a RAC database is the same as for a single instance database, you just need to run it on one of the instances, without adding an instance filter.
At the end of the capture, you should generate an html summary, and export related AWR data, for that, you’ll need the ID of you capture.
To prepare the replay you need two things :
The captured raw files must be processed by the database, before the capture could be replayed, this step could take some time depending of the size of the workload raw files.
Then we initiate the replay (just an initiation, the replay is not yet started).
Checking the status of the replay.
Based on this calibration, and after checking the cpu and memory available, I will use 56 clients (56 x 152 MB = 8.5G of minimal memory needed for the clients : Ok for me).

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Amazon

379

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How Monzo Bank reduced cost of TTL from time series index tables in Amazon Keyspaces

Monzo Bank reduced costs on TTL from time series index tables in Amazon Keyspaces by using bulk deletes to eliminate the use of TTLs.
Monzo's event index table indexes events on its platform over time with a large volume of events flowing through its platform.
Using the TTL feature for many years proved to be expensive for Monzo as the write throughput increased with the bank's growth.
Monzo used a bucketing mechanism to efficiently distribute rows across multiple tables for dynamic dropping of old tables and creating new tables as needed.
Table management code was needed to be written for creating, dropping and scaling tables and sharding code was written for routing queries.
Amazon Keyspaces recently lowered its TTL cost by 75%, yet this pattern can save even more costs.
Monzo slowly ramped up dual-writing to the new index table in shadow mode to test the suitability and production readiness of the write path.
Finally, all of the rows in the old index table expired because of their Time to Live settings before we could transition to the new index table fully.
The new sharded index table has been running in Monzo's production environment without issue for several months.
Code complexity was introduced as a result of this change, but Monzo believes that the cost savings are worth the added complexity.

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Cloudblog

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How Memorystore helps FanCode stream 2X more live sports

FanCode has migrated to Google Cloud and adopted Memorystore for Redis Cluster to deliver low-latency and personalised sports content to millions of fans across India.
FanCode delivers live sports events with unparalleled coverage, real-time scores, player statistics and tailored recommendations based on fan preferences, viewing history and regional interests.
Redis plays a crucial role in the FanCode backend infrastructure, providing the necessary caching layer to support fast data retrieval, low-latency streaming, and real-time processing.
FanCode's application infrastructure, hosted entirely on Google Cloud, uses Memorystore for Redis Cluster to maintain fault tolerance, performance, and scalability.
The fully managed service integrates seamlessly with FanCode's Google Cloud environment, so they can scale easily as demand increases.
During live events, where large spikes in concurrent viewers require both high performance and reliability, caching layer is especially crucial.
FanCode is breaking new ground in sports media, transforming fan engagement into a more immersive experience with personalised recommendations based on real-time data processing.
Google Cloud's strong SLA has been critical during high-stake events, delivering uninterrupted, high-quality streaming that keeps fans coming back for more.
FanCode's infrastructure supports over 15,000 live events each year, which is more than double the previous volume, thanks to Google Cloud's scalability and reliability.
FanCode is meeting the demands of modern sports enthusiasts and achieving its business goals with a robust, integrated infrastructure.

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Dev

198

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Understanding Data Partitioning vs. Sharding: Key Concepts for Effective Data Management

Data partitioning refers to the logical division of a dataset into smaller, more manageable pieces based on some criteria.
Data sharding refers to the horizontal partitioning of data across multiple physical nodes or servers in a distributed system.
Key differences between data partitioning and data sharding include scope, purpose, physical location, and independence.
Partitioning is suitable for single database systems to improve query performance, while sharding is used for scalability and fault tolerance in distributed systems.

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Medium

140

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Provably Alpha: The Future of Verifiable Analytics is Here

Provably V1 simplifies the complex world of Zero-Knowledge (ZK) proofs, empowering you to manage access to your data with confidence, knowing it remains private.
The release addresses three issues: giving users control over their data, automating privacy and delivering fast analytics, and enabling analytics with verifiability.
Provably allows users to easily connect their database and select the tables they want to make available for analytics. Future updates will introduce API access and email invites for others to run queries on the data.
With Provably, data privacy is maintained as the insight seeker can run SQL queries without the need for lengthy data preparation or revealing the original data.

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Dev

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They Stopped Using Load Tests - And Here Is Why

Load tests have been dropped by many companies due to numerous drawbacks.
Load tests are time-consuming and challenging to create and maintain.
Compliance with regulations and randomness complicate load testing.
Instead, companies should focus on early issue detection through observability techniques.

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Javacodegeeks

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Tear Down HSQLDB Database After Tests

Testing is a critical aspect of software development, and maintaining a clean test environment ensures accurate and reliable results.
To set up the test environment, include the necessary dependencies in your pom.xml, such as spring-boot-starter-data-jpa, spring-boot-starter-test, and hsqldb.
This article explores how to effectively manage database states in tests, focusing on how to set up and tear down Java HSQLDB databases.
The article explores the following solutions: Using spring.jpa.hibernate.ddl-auto and @DirtiesContext to reload the application context. Using @Sql annotation to reset the database state after every test with custom SQL scripts.
The spring.jpa.hibernate.ddl-auto=create-drop ensures the database schema is recreated for each test suite.
This approach uses the hibernate.hbm2ddl.auto property to recreate the schema on application startup and the @DirtiesContext annotation to reload the Spring context after each test.
The @Sql annotation provides a way to execute custom SQL scripts before or after each test.
When the application is executed, the test suite runs all the defined test cases, ensuring the database behaves as expected.
From leveraging hibernate.hbm2ddl.auto with @DirtiesContext for schema recreation to employing @Sql annotations with custom scripts for flexible cleanup, each method offers unique benefits tailored to specific use cases.
This article covered how to tear down an HSQLDB database in Java.

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Dev

191

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Mastering SQL DISTINCT: Removing Duplicates Made Simple

The DISTINCT keyword in SQL is used to remove duplicate rows from the result set of a query.
By adding the DISTINCT keyword, SQL filters out these duplicates, keeping only one occurrence of each unique combination of values in the specified columns.
Using DISTINCT can slow down queries, especially on large datasets, as SQL must scan and compare rows to filter duplicates.
The DISTINCT keyword is a simple yet powerful tool to eliminate duplicate rows in SQL query results.

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Dev

232

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SQL UNION vs UNION ALL: Key Differences Explained

UNION combines the results of two or more SELECT statements into a single result set and removes duplicate rows.
UNION ALL also combines the results of two or more SELECT statements into a single result set, but retains all rows, including duplicates.
UNION performs an implicit DISTINCT operation to remove duplicates, which can be slower for large datasets.
UNION ALL is faster than UNION since no duplicate-checking occurs.

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Dev

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Mastering SQL Joins: LEFT JOIN vs RIGHT JOIN Explained with Examples

LEFT JOIN and RIGHT JOIN are types of SQL OUTER JOINs.
The LEFT JOIN returns all rows from the left table, and the matched rows from the right table.
The RIGHT JOIN returns all rows from the right table, and the matched rows from the left table.
LEFT JOIN ensures all rows from the left table appear, while RIGHT JOIN ensures all rows from the right table appear.

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Dev

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INNER JOIN vs OUTER JOIN: Understanding SQL Joins in Depth

INNER JOIN returns only the rows that have matching values in both tables.
OUTER JOIN includes rows from one or both tables, even if there is no match.
There are three types of OUTER JOINs: LEFT JOIN, RIGHT JOIN, and FULL JOIN.
The choice between INNER JOIN and OUTER JOIN depends on the requirements of your query.

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Amazon

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Reduce latency and cost in read-heavy applications using Amazon DynamoDB Accelerator

Amazon DynamoDB is a fully managed, serverless, NoSQL database that can scale applications at any level.
Amazon DynamoDB Accelerator (DAX) is an in-memory cache for DynamoDB that can improve read latency in applications that are heavily read-dominant, like news media sites, ecommerce sites, etc.
The article explains how applications can achieve lower read latency with the use of DAX through examples.
In the example shared, implementing DAX showed a 19% reduction in latency for GetSingleProduct requests and a 15% decrease in latency for GetListOfProducts requests.
DAX maintains an item cache and a query cache that stores read operation data from a DynamoDB table, this allows DAX to return results to an application without looking up DynamoDB.
The usage of DAX can also reduce the cost of read operations for DynamoDB tables in read-heavy applications.
DynamoDB tables configured in on-demand capacity mode are estimated to cost around $1150 per month, while those in provisioned mode are $474.
Using DAX with a provisioned mode table can decrease cost by 21% and by 67% for those using on-demand mode tables.
A DAX cluster is charged per node usage by the hour, and its performance depends on the size of the cluster used.
DAX can be a cost-effective solution for read-heavy applications that utilize DynamoDB and serve a large user base.

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Dev

321

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Java JDBC + IntelliJ + SQLite - A Beginner's Walkthrough

A beginner's walkthrough on setting up Java JDBC with IntelliJ and SQLite database.
No build tool required (Maven or Gradle) for this setup.
Download SQLite command-line shell for creating and using a database file as the JDBC connection.
Learn how to use JDBC and SQLite through online tutorials and documentation.

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Amazon

397

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Authenticate Amazon RDS for Db2 instances using on-premises Microsoft Active Directory and Kerberos

Amazon RDS for Db2 makes it easy to set up, operate, and scale Db2 deployments in the cloud and automate database administration tasks.
Enterprise customers can enable single sign-on (SSO) and centralized Kerberos authentication of database users using Microsoft Active Directory (AD).
This post demonstrates extending your existing AD infrastructure and Kerberos authentication to Amazon RDS for Db2.
Amazon RDS supports Kerberos authentication for various database engines and AWS Regions for centralized database management.
The solution involves using AWS Managed Microsoft AD to establish a forest-level outgoing trust to an on-premises AD.
The solution architecture includes deploying Amazon RDS for Db2 instance, creating local permissions for the Admin user in RDS for Db2 database and optionally, creating local permissions for the DBADMIN group.
Users can log in to RDS for Db2 instances using Kerberos enabled SSO capabilities and on-prem AD can be used to centrally manage database authentication and authorization for RDS DB instances.
The solution can be extended for other Amazon RDS database engines that support Kerberos including PostgreSQL, MySQL, Oracle, and SQL Server.
This solution was tested using Terraform and the code is available in the accompanying GitHub repo.
To conclude, this post has demonstrated how to extend your existing Microsoft AD infrastructure to Amazon RDS for Db2 and enable Kerberos authentication, allowing users to log in to RDS for Db2 instances using existing SSO capabilities.

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