{"id":664935,"date":"2020-06-08T13:57:41","date_gmt":"2020-06-08T20:57:41","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=664935"},"modified":"2020-06-08T13:58:38","modified_gmt":"2020-06-08T20:58:38","slug":"fishstore-fast-ingestion-and-indexing-of-raw-data","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/fishstore-fast-ingestion-and-indexing-of-raw-data\/","title":{"rendered":"FishStore: Fast Ingestion and Indexing of Raw Data"},"content":{"rendered":"
The last decade has witnessed a huge increase in data being ingested into the cloud from a variety of data sources. The ingested data takes various forms such as JSON, CSV, and binary formats. Traditionally, data is either ingested into storage in raw form, indexed ad-hoc using range indices, or cooked into analytics-friendly columnar formats. None of these solutions is able to handle modern requirements on storage: making the data available immediately for ad-hoc and streaming queries while ingesting at extremely high throughputs. We demonstrate FishStore, our opensource concurrent latch-free storage layer for data with flexible schema. FishStore builds on recent advances in parsing and indexing techniques, and is based on multi-chain hash indexing of dynamically registered predicated subsets of data. We find predicated subset hashing to be a powerful primitive that supports a broad range of queries on ingested data and admits a higher performance (by up to an order of magnitude) implementation than current alternatives.<\/p>\n","protected":false},"excerpt":{"rendered":"
The last decade has witnessed a huge increase in data being ingested into the cloud from a variety of data sources. The ingested data takes various forms such as JSON, CSV, and binary formats. Traditionally, data is either ingested into storage in raw form, indexed ad-hoc using range indices, or cooked into analytics-friendly columnar formats. 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We have a \u201ckitchen sink\u201d of solutions available today, each optimized for a specific workload. The SimpleStore project aims at simplifying the use of storage for modern cloud, edge, serverless, and big data applications. Our recent presentation at HPTS overviews the broader research project. 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