One thing that can really wreck your performance in Cassandra and the similar YugabyteDB YCQL is large partitions due to an imbalanced key. Without the robust nodetool commands of Cassandra, it can be challenging to find these large partitions in YugabyteDB.

dsbulk is a tool used for migrating data, and YugabyteDB has a fork that takes into consideration slight differences from Cassandra. That tool can be leveraged to list the top largest partitions.

Today’s global and distributed applications often need to serve user requests from a single data source across different regions. While providing data scaling and protection against network outages, ensuring low-latency access to data is critical for providing a seamless user experience. YugabyteDB, a distributed SQL database, is designed to handle global data workloads efficiently. In this blog post, I’ll share some techniques to optimize read and write latency in a multi-region YugabyteDB cluster.

Modern distributed databases split large tables into tablets to enable parallel processing and efficient data distribution. Finding the right tablet size impacts everything from query performance to operational overhead. Let’s explore how to approach tablet sizing systematically to achieve optimal performance.

Understanding Tablet Impact

Each tablet in your distributed database represents an independent unit of data distribution. When you create tablets, you influence system behavior at multiple levels. The database uses tablets to parallelize operations, manage resources, and handle data growth. Your tablet strategy directly affects query response times, write throughput, and overall system health.

I’m writing this the night before we’re going to get some very bad weather. In North Carolina this time of year, that could mean an ice storm. Ice coats all the big and little branches on all the trees, and then they start to shatter onto roofs and overhead cables. I’ve lived all over the lower 48 and in Alaska, and ice storms are by far the worst, especially in a state that doesn’t bury the electrical lines.

Timing of seasonal demand depends on the industry, but a cyclical increase in traffic applies to all industries. Maybe your cycle is shorter than a full year. Or maybe it’s related to things like weather patterns or fashion.

You know when your business gets the most traffic.

• Retail? Black Friday/Cyber Monday.
• Health and fitness? New Year’s Day.
• Pizza restaurant? Halloween and Thanksgiving, surprisingly.

You do know your business well. But do you know how to make sure your database infrastructure can keep up with traffic during these busy periods?

A distributed database is designed to withstand outages to a good degree. However, you should also maintain backups in case of “oops” scenarios like a dropped table.

The yb-admin tool can be used to manage snapshots. Here’s a brief walkthrough.

Some caveats about using snapshots… They are stored on the same server, so this method doesn’t protect against file system corruption. Also, this doesn’t snapshot the schema, just data.

If you don’t already have a test environment, check out a quick test setup here https://github.com/dataindataout/xtest_ansible.

Memory configuration in YugabyteDB for YSQL workloads involves partitioning among the tserver, master, and postgres processes, each with default ratios. Adjusting these ratios based on workload characteristics helps avoid out-of-memory events. Monitoring memory usage is crucial, and tuning parameters like max_connections, work_mem, and temp_file_limit can optimize both performance and resource utilization.

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At DSS 2021, I provided a comprehensive orientation to monitoring YugabyteDB, focusing on how to interpret and leverage built-in metrics for operational visibility.

Three key dimensions of database monitoring:

• Uptime (Alerting): Ensuring the system is running and healthy through critical alerts.
• Performance (Trending): Tracking historical performance to detect changes and optimize queries.
• Capacity Planning (Forecasting): Using metrics to project future infrastructure needs based on current utilization and expected growth.

Types of monitoring:

Automation tools can streamline the planning, registration, and management of Girl Scout events, saving time for troop leaders. Practical examples might include automating communications, scheduling, or badge tracking. This approach helps modernize event management and reduce administrative overhead, making it ideal for volunteers looking to simplify their workflows.

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Migrating Oracle workloads to Google Cloud’s Bare Metal Solution (BMS) offers benefits like reduced rewrites, familiar hardware, and simplified licensing. Challenges include server sizing, OS changes, and database upgrades. Careful planning and consolidation are key for large databases, and BMS is well-suited for organizations aiming to minimize downtime and risk during migration.

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