April 2022 Summaries

The Aerospike Standup newsletter, crafted by developers for developers, offers a comprehensive overview of recent advancements and community interactions surrounding Aerospike. It highlights new blog posts on topics such as Aerospike Database 6 features, time series APIs, and batch operations, authored by experts like Ronen Botzer and Kiran Matty. Addressing community inquiries, the newsletter provides insights into data recovery and record counting within TTL ranges. Additionally, it includes release notes for various Aerospike components, covering updates from early to late April across different clients and tools, showcasing the ongoing development and support within the Aerospike ecosystem.

Aerospike has released version 6 of its database server, which includes new features such as Partitioned Secondary Index Queries, Batch Anything, and JSON Document Models. The update builds on the improvements made in Aerospike Database 5, with a focus on queries. The new query subsystem allows for massively parallelized primary index (PI) and secondary index (SI) queries, improving performance and throughput. Additionally, server version 6.0 introduces support for document data models through the Document API and its ability to index elements nested at any depth. Furthermore, batch operations have been completed, allowing developers to batch anything in their applications, including reads, writes, updates, deletes or UDFs. This results in a more efficient use of network traffic and faster data ingest.

Aerospike has announced the release of Aerospike Database 6, which brings predictable sub-millisecond performance, low latency, and unlimited scale to more data models across the enterprise. This general availability release introduces native support for JSON document models at gigabyte to petabyte scale, allowing for complex SQL queries with massively parallel secondary indexes. The enhanced database also includes JSONPath query support, enabling developers to work in a familiar environment and language. Aerospike Database 6 meets the demand for accurate processing of larger, more complex data sets generated by the explosive growth of data, connected devices, and an insatiable appetite for instant, accurate outcomes.

Aerospike has announced the release of version 6.0 of its database engine as the core of the Aerospike Real-time Data Platform, bringing predictable sub-millisecond performance and low latency to more data models across the enterprise at an affordable cost. This release provides native support for JSON document models, enabling the processing of complex queries and large document models at any scale. The enhanced database also includes JSONPath query support, allowing developers to work in a more familiar environment and language. With this update, Aerospike meets the growing demand for accurate processing of larger, more complex data sets generated by the increasing amount of data and connected devices.

Aerospike Database 6 introduces several new features for developers, including partitioned queries, query pagination, and enhanced batch processing capabilities. The update comes with significant changes, such as the renaming and removal of configuration parameters and statistics tied to previous server versions, reflecting a streamlined query subsystem. Notably, parameters related to the server 5.x query subsystem have been removed, and certain error codes have been updated, which may impact applications. The update also enhances logging by adding histogram log lines for primary and secondary index queries, replacing previous scan log lines, and updating audit trail actions from "scan" to "query." These changes aim to improve performance and maintainability while ensuring compatibility with newer client requirements.

The text provides information about various blog posts and events related to Aerospike. It mentions the Summit 2022 agenda which includes keynotes, panels, and fireside chats. Additionally, it highlights several blog articles discussing topics such as achieving a perfect golden record with graph data, transitioning from Redis to Aerospike for efficient management of 45 billion records, real-time decision making using AI, and questioning the use of Redis as a cache.

Aerospike Database now supports batch writes, which can be executed in synchronous and asynchronous mode, allowing applications to leverage improved ingest and update throughputs by combining multiple requests into a single operation. The execution flow of a request involves combining or batching requests, obtaining a connection to the server, sending the request, receiving the response, processing the response, and executing inline processing, retries, and optimal batch size. Synchronous batch requests can be sent as soon as they are formed and receive responses all at once, while asynchronous batch requests allow for more efficient use of thread resources but may incur context switching overhead. The combination of single-record vs batch and sync vs async operations offers various tradeoffs in terms of resource utilization, throughput, and ease of programming. Pipeline processing is also an option that offers best resource utilization without having to wait for responses or obtain new connections. Additionally, Aerospike supports multi-key operate, general batch operate, synchronous general batch request, and other batch capabilities, while ensuring entire batches are processed and providing features such as key field matching, overall batch status, and filter expressions alignment with batch operation semantics.

The Aerospike Spark connector enables massively parallel storage to build high-throughput and low-latency ETL pipelines by leveraging the hybrid memory architecture of Aerospike, which is ideal for real-time environments managing terabyte to petabyte data volumes. The connector loads data into a Spark streaming or batch DataFrame in a massively parallel manner, allowing users to process data using Spark APIs supported in multiple languages. It also enables efficient writing of DataFrames to Aerospike via the corresponding Spark write API. The connector allows applications to parallelize work on a massive scale by leveraging up to 32,768 Spark partitions to read data from an Aerospike namespace. By pushing down predicates in queries, the connector optimizes query performance by limiting data movement between the Spark cluster and the database, using Aerospike Expressions to create powerful and efficient filters that significantly limit data transfer between Aerospike and Spark clusters. However, the existing Spark filter class has limitations on the number of Aerospike expressions it can generate and push down to the database, which is addressed by a side-channel approach that inserts user-provided Aerospike expressions into the query plan in the code generation phase using the `.option()` method. This approach allows users to bring the full power of Aerospike Expressions to bear without altering the Spark query planning process, resulting in significant performance gains when running Spark queries against large datasets stored in Aerospike.

Connected TV (CTV) is emerging as a critical component of the media mix, with an expected 113 million CTVs in the U.S. by 2024. As more people cut the cord and turn to streaming services, advertisers have new opportunities to place customized messages before viewers. The market for CTV ad spending is predicted to hit nearly $35 billion by 2025, driven by factors such as increased time spent on CTVs, device companies emphasizing their ad business, and the growth of ad-supported video platforms. However, challenges remain in targeting audiences effectively and measuring advertising performance due to fragmentation and a lack of consistent ID across streaming apps. As opportunities emerge to shift advertising dollars to CTV, marketers need to leverage best-in-breed targeting methodologies, measure their return on ad spend (ROAS), and use analytics to understand the results and optimize accordingly.

The rapid adoption of Connected TV (CTV) in the U.S., projected to reach 113 million units by 2024, presents a lucrative opportunity for advertisers to deliver tailored messages through internet-based content streaming, though challenges persist. As cord-cutting accelerates, with traditional cable or satellite TV viewership dropping significantly, platforms like Roku, Vizio, and Amazon are capitalizing on the shift by enhancing their ad-supported services and technologies, such as Vizio's Project OAR for standardized addressable TV. CTV ad spending is forecasted to soar to nearly $35 billion by 2025, driven by increased consumer screen time and the growth of ad-supported video platforms. However, advertisers face obstacles like fragmented identifiers across devices and shared streaming accounts, complicating audience targeting and measurement. Companies like Aerospike are offering real-time data platforms to handle large data volumes, providing faster insights and a better return on investment in this evolving CTV landscape. As the industry adapts, innovative targeting methodologies and comprehensive measurement strategies are crucial for maximizing the effectiveness of CTV advertising campaigns.