April 2022 Summaries

The text discusses multi-tenancy, a common approach in SaaS companies where multiple customers share the same database. The article explores different models of multi-tenancy, including one schema per tenant, one table per tenant, and hybrid approaches. It highlights the benefits and drawbacks of each model, with SingleStoreDB being mentioned as a solution that supports these approaches. The text also discusses day-to-day operations such as onboarding new customers, updating schemas, analyzing customer behavior, and migrating data across locations. It concludes that SingleStoreDB delivers reliable, elastic scalability, high query ingest and concurrency, and real-time analytics capabilities for multi-tenancy use cases.

The text discusses the increasing cyber security risk brought about by the war in Ukraine, with companies such as Akamai and Palo Alto Networks noting a surge in DDoS attacks and spear-phishing attempts. These attacks are often leveraged by threat actors to exploit geopolitical tensions and target organizations in Europe, particularly those related to Russia or Ukraine. The text also highlights the importance of cybersecurity best practices, such as updating security measures and patching vulnerabilities. Additionally, it features a conversation with Ilya Gulman from Twingo, who discusses the challenges of modern architectures and the benefits of consolidating multiple databases into a single platform, citing SingleStoreDB Self-Managed as an example. Finally, it mentions how Armis Security has implemented SingleStoreDB Self-Managed to streamline their cybersecurity solutions and improve performance.

The current state of databases is not designed to support fast data ingestion and processing, with most being geared towards transactional workloads rather than big data. However, in markets driven by digital interactions, speed at scale is no longer enough; customers need highly performant platforms that can handle massive volumes of data without lags in availability. SingleStore uses massive parallel processing (MPP) to scale out database architectures, adding nodes as workload demands increase, and has recently completed TPC benchmarking studies with Dell Technologies to demonstrate its performance potential. The partnership highlights the ability of SingleStore to accelerate data ingest, reduce query latency, and increase concurrency, while also providing a modern IT foundation for data-intensive applications.

This edition of SingleStore's engineer showcase puts the spotlight on Charlie Joseph, a London-based software engineer and entrepreneur who has been programming since he was 13. Charlie is working on Analyse, a game analytics SaaS that uses SingleStore as its favored database due to its ability to handle high volumes of data without requiring extensive migration efforts. Charlie praises SingleStore's MySQL connector for making it easy to implement the database in his application and appreciates the self-hosting option, which has been crucial for his start-up's budget constraints. He also favors Laravel and Vue.js as frameworks and tools, respectively, due to their flexibility and ease of use. Charlie advises developers to focus on functional development over optimization, emphasizing the importance of shipping code quickly and refactoring later. Overall, Charlie credits SingleStore with providing a solid out-of-the-box solution that has streamlined his development process and enabled him to focus on core application features rather than infrastructure concerns.

It's crucial to test an application with the same database used in production, and SingleStoreDB can be run locally using Docker or other compatible container platforms. To set up SingleStoreDB for local development, one needs a license key, defines environment variables, runs the Docker container, checks its status, and logs. After getting it working locally, it's essential to integrate SingleStoreDB into Github Actions workflows by creating an encrypted secret, adding a service container definition, and running a sanity check using a MySQL client. This ensures that database schema and queries are thoroughly tested and automated, making the development and deployment process more efficient and secure.

The new SingleStoreDB tool truck features numerous performance and scalability improvements, including Flexible parallelism, which allows queries to use all cores on a machine regardless of the number of partitions. The release also includes Spilling for hash group-by, select-distinct and count-distinct operations, which writes part of the in-memory hash table to disk if it exceeds available memory, allowing queries to run even if they hit the memory limit. Additionally, SingleStoreDB upgraded its LLVM code generation framework to version 10, resulting in improved query performance. The tool truck also introduces Materialized CTEs, row-level decoding for string columns, faster upgrades via reduced effort for codegen, and a new SET statement for user-defined session variables. Other features include Matching expressions to computed columns, VECTOR_SORT and other vector functions, ISNUMERIC, SELECT FOR UPDATE – now for Universal Storage, SECRET(), and various storage improvements such as backup with split partitions for unlimited storage DBs and drop milestones.

Stored procedural objects, including stored procedures, user-defined scalar-value functions (UDFs), user-defined table-valued functions (TVFs), and user-defined aggregate functions (UDAFs), are available in SingleStore to achieve performance and business objectives by storing compiled code inside the database. Stored procedures can manipulate data in a table and return single values like UDFs, as well as query-type values. Functions like UDFs and TVFs provide a wide range of capabilities for customer logic sets, while external functions are maintained and executed outside SingleStore's internal services. Aggregates, such as UDAFs, enable the execution of programmatic logic in the function body to return scalar-type values.

SingleStoreDB, a distributed database, offers elasticity and scalability with its flexible parallelism (FP) feature, which enables transparent scalability without manual sharding. The feature is enabled by default for Singlestore Helios users but off by default for SingleStoreDB Self-Managed users. FP allows individual queries to take advantage of all available cores, even if the database has only a few partitions per leaf node. This eliminates the need for manual partitioning and sharding at the application level. With FP, developers can focus on writing applications rather than managing data platforms, saving development time and resources. The feature is part of SingleStoreDB's long-term strategy to become an ultimate elastic relational database for modern applications.