Browse Tag: Append-Only

Yet Another Cache but for the Streaming World

Traditional cache solutions treat each entry as an immutable blob of data, which poses problems for the append-heavy ingestion workloads that are common in Pravega. Each Event appended to a Stream would either require its own cache entry or need an expensive read-modify-write operation to be included in the Cache. To enable high-performance ingestion of events, big or small, while also providing near-real-time tail reads and high-throughput historical reads, Pravega needs a specialized cache that can natively support the types of workloads that are prevalent in Streaming Storage Systems.

The Streaming Cache, introduced in Pravega with release 0.7, has been designed from the ground up with streaming data in mind and optimizes for appends while organizing the data in a layout that makes eviction and disk spilling easy.

Not all caches are created equal. It is essential to choose a cache that fits the requirements of the system where it will be used, and streaming solutions are no exception to that rule. In this blog post, we describe an innovative way to look at caching that works well with streaming use cases. Continue Reading

Segment Attributes

The ability to pipeline Events to the Segment Store is a key technique that the Pravega Client uses to achieve high throughput, even when dealing with small writes. A Writer appends an Event to its corresponding Segment as soon as it is received, without waiting for previous ones to be acknowledged. To guarantee ordering and exactly once semantics, the Segment Store requires all such appends to be conditional on some known state, which is unique per Writer. This state is stored in each Segment’s Attributes and can be atomically queried and updated with every Segment operation.

Over time, Attributes have evolved to support a variety of use cases, from keeping track of the number of Events in a Segment (enabling auto-scaling) to storing a hash table index. The introduction of Table Segments (key-value stores which contain all of Pravega’s Stream, Transaction and Segment metadata) required the ability to seamlessly manage tens of millions of such Attributes per Segment.

This blog post explains how Segment Attributes work under the hood to provide an efficient key-value store that represents the foundation for several higher-level features. It begins with an overview of how Pravega Writers use them to prevent data duplication or loss and follows up by describing how Segment Attributes are organized as B+Trees in Tier 2 using innovative compaction techniques that reduce write amplification. Continue Reading