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 […]
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 […]
Streaming applications typically need to process the events as soon as they arrive. For example, being able to quickly react to events in applications such as fraud detection, manufacturing error detection can result in massive savings. However, due to the limitation of storage systems not being able to handle large numbers of small writes, producers […]
Introduction Reading and writing is the most basic functionality that Pravega offers. Applications ingest data by writing to one or more Pravega streams and consume data by reading data from one or more streams. To implement applications correctly with Pravega, however, it is crucial that the developer is aware of some additional functionality that complements […]
Stream On with Pravega On stage: Pravega Stream processing is at the spotlight in the space of data analytics, and the reason is rather evident: there is high value in producing insights over continuously generated data shortly rather than wait for it to accumulate and process in a batch. Low latency from ingestion to result […]
Driven by the desire to shrink to zero the time it takes to turn massive volumes of raw data into useful information and action, streaming is deceptively simple: just process and act on data as it arrives, quickly, and in a continuous and infinite fashion. For use cases from Industrial IoT to Connected Cars to […]