Most content-based websites, like Yahoo News, HuffPost, or any given news site, organize their stories according to subject matter or in some similar way. You can imagine that websites with a huge amount of stories must need an automated method to filter or categorize them as the content is ingested into their systems. For example, algorithms that power Yahoo News label news articles with tags e.
This well-known process of labeling content with all its relevant tags is known as Multilabel Learning MLL. Up to now, whenever scientists and engineers use MLL to create their own specific models to label content however they like, they have used datasets that have pre-computed features like bag-of-words, or dense representations like doc2vec.
While Completing the square formula yahoo dating MLL approaches rely on given features, DocTag2Vec operates on raw text and automatically learns the best features of that text by embedding both documents and the tags in the same vector space.
Inference is then done via a simple nearest-neighbor based approach. DocTag2Vec relies on training data that is composed of the raw text of every document and the labels associated with them. There are many standard datasets available for MLL, but all of them directly provide Completing the square formula yahoo dating and not the actual text of the documents.
This allows researchers to work on new algorithms that directly use the provided features but without improving the features themselves. Our YNMLC corpus provides raw text so that researchers can extract their own features that are best for their algorithms.
Apart from that, to the best of our knowledge, our corpus is the only one that provides a ranking of the labels for each document in terms of its importance. The corpus contains 48, articles that are tagged by any subset of labels.
These tags correspond to Vibes akin to topics in the Yahoo Newsroom app. MLL is an area of research that we have applied to labeling news stories. MLL can also be used to label music, videos, blog posts, and virtually any other type of online content.
Ideally, NoSQL applications would like to enjoy the speed of in-memory databases without giving up on reliable persistent storage guarantees.
Our Scalable Systems research team has implemented a new algorithm named Accordion, that takes a significant step toward this goal, into the forthcoming release of Apache HBase 2. HBasea distributed KV-store for Hadoop, is used by many companies every day to scale products seamlessly with huge volumes of data and deliver real-time performance.
Accordion is a complete re-write of core parts of the HBase server technology, named RegionServer. It improves the server scalability via a better use of RAM.
Namely, it accommodates more data in memory and writes to disk less frequently. This manifests in a number of desirable phenomena. With Accordion, they all get improved simultaneously.
We stress-tested Accordion-enabled HBase under a variety of workloads. Our experiments exercised blends of reads and writes, as well as different key distributions heavy-tailed versus uniform. We witnessed performance improvements across the board. An HBase region is stored as a sequence of searchable key-value maps.
The topmost is a mutable in-memory store, called MemStore, which absorbs the recent write put operations.
Once a MemStore overflows, it is flushed to disk, creating a new HFile. HBase adopts multi-versioned concurrency control — that is, MemStore stores all data modifications as separate versions. Multiple versions of one key may therefore reside in MemStore and the HFile tier. A read get operation, which retrieves the value by key, scans the HFile data in BlockCache, seeking the latest version. To reduce the number of disk accesses, HFiles are merged in the background. This process, called compactionremoves the redundant cells and creates larger files.
Completing the square formula yahoo dating, their traditional design makes no attempt to compact the in-memory data. This stems from historical reasons: With recent changes in the hardware landscape, the overall MemStore size managed by RegionServer can be multiple gigabytes, leaving a lot of headroom for optimization. This work pattern decreases the frequency of flushes to HDFS, thereby reducing the write amplification and the overall disk footprint.
With fewer flushes, the write operations are stalled less frequently as the MemStore overflows, and as a result, the write performance is improved. Less data on disk also implies less pressure on the block cache, higher hit rates, and eventually better read response times.
Finally, having fewer disk writes also means having less compaction happening in the background, i. All in all, the effect of in-memory compaction can be thought of as a catalyst that enables the system to move faster as a whole. Accordion currently provides two levels of in-memory compaction: The former applies generic optimizations that are good for all data update patterns.
The latter is most useful for applications with high data churn, like producer-consumer "Completing the square formula yahoo dating," shopping carts, shared counters, etc.
All these use cases feature frequent updates of the same keys, which generate multiple redundant versions that the algorithm takes advantage of to provide more value. Future implementations may tune the optimal compaction policy automatically.
Accordion replaces the default MemStore implementation in the production HBase code. Contributing its code to production HBase could not have happened without intensive work with the open source Hadoop community, with contributors stretched across companies, countries, and continents.
The project took almost two years to complete, from inception to delivery. Accordion will become generally available in the upcoming HBase 2.