[@IndeedEng] Imhotep Workshop
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Imhotep is Indeed's open-source analytics platform that allows for easy upload and compression of data as well as fast, interactive querying. It uses an SQL-like query language called IQL to perform aggregate analytics on datasets within specified date ranges and with optional filters, groupings, and metrics. The platform aims to provide an interactive experience where questions can be quickly refined, show ground truth data through a web interface, and instantly share insights through caching.
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3. The merging process involves loading, sorting, and rewriting levels to consolidate and propagate deletions and updates between levels.
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RocksDB is an embedded key-value store written in C++ and optimized for fast storage environments like flash or RAM. It uses a log-structured merge tree to store data by writing new data sequentially to an in-memory log and memtable, periodically flushing the memtable to disk in sorted SSTables. It reads from the memtable and SSTables, and performs background compaction to merge SSTables and remove overwritten data. RocksDB supports two compaction styles - level style, which stores SSTables in multiple levels sorted by age, and universal style, which stores all SSTables in level 0 sorted by time.
LSM
Este documento describe la Lengua de Señas Mexicana. Explica que es una lengua gestual basada en el uso de las manos, los ojos, la boca y el cuerpo, con reglas gramaticales complejas y un vocabulario en aumento. También discute algunos mitos sobre las lenguas de señas, aclarando que son lenguas naturales y no meros códigos, y que cada lengua de señas es única a pesar de compartir el país con una lengua oral. Finalmente, ofrece consejos para comunicarse con personas sordas que usan
LSM Trees
The document discusses LSM-trees, which are data structures used in Cassandra and other databases. An LSM-tree improves write performance by storing data in an in-memory tree and writing large batches to disk. It supports fast reads by merging data from memory and disk. The document provides examples of how Cassandra uses LSM-trees to handle writes, reads, and compaction of SSTables from memory to disk.
[@IndeedEng] Managing Experiments and Behavior Dynamically with Proctor
Video available at: http://youtu.be/Q1T5J0KXUwY
At this very moment, Indeed is running more than one hundred A/B experiments. In previous @IndeedEng talks, we have discussed how we use A/B testing to develop better products.
In this tech talk, software engineer Matt Schemmel and product manager Tom Bergman describe Proctor, the system we developed to define and manage all of these experiments. They explain how we use Proctor to target users using data-driven rules, adjust experiments on-the-fly, and ensure clean results for multi-variate tests. Over time, Proctor has evolved from a system designed for managing experiments to one that manages overall system behavior through dynamic "feature toggle" functionality. Matt and Tom also share lessons we have learned from years of experimenting at web scale.
Matt Schemmel is a Senior Software Engineer working primarily on our Resume products.
Tom Bergman is a Product Manager currently working on our Aggregation systems. He previously helped evolve many of Indeed's data analysis tools, and also helped us launch and grow our sites in Japan, Korea, and China.
失敗から学ぶ データ分析グループの チームマネジメント変遷 （デブサミ2016） #devsumi
デブサミ2016 #devsumi で話させていただいた資料です。
http://event.shoeisha.jp/devsumi/20160218/session/1007/
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Bios:
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Authors:
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Nathan Kurz, Verse Communications
Daniel Lemire, LICEF, Universite du Québec
Paper accepted to the International Symposium on Web Algorithms (iSWAG), 2015.
Blog post: http://engineering.indeed.com/blog/2015/03/vectorized-vbyte-decoding-high-performance-vector-instructions/
Abstract:
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Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
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The Microsoft 365 Migration Tutorial For Beginner.pptx
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[@IndeedEng] Imhotep Workshop
- 3. @IndeedEng Workshop: Interactive Analytics with Imhotep
- 4. Tom Bergman Product Manager
- 6. Does this thing work???
- 9. What is Imhotep? Imhotep is Indeed’s highly scalable open-source analytics platform.
- 10. Imhotep open source included: ● Imhotep Daemons ● Imhotep Query Language (IQL) ● IQL Web Client ● TSV/CSV Uploader
- 11. What does Imhotep do? ● Easy upload & compression ● Fast, Interactive queries
- 13. Interactive ● Quickly refine your questions
- 14. Time to the right question SOME TIME LATER… Oh, bummer. Wrong question. Let’s try again. Nope. Nope. YES! Next question? Hadoop and the Hadoop elephant logo are trademarks of the Apache Software Foundation.
- 15. Ask all the questions! Cool! Really? Wow... Awesome Oh… Ah! INSIGHT! …
- 16. Ground Truth ● Data should not be down-sampled
- 18. Show me the data ● Web-based to facilitate sharing
- 21. Cache Rules Everything Around Me ● Instantaneous sharing
- 22. Easy Access ● Easily queryable
- 23. Imhotep Data Structures ● Dataset > ● Document > ● Field > ● Term > ● DB Table ● Denormalized Row ● Column ● Value
- 24. Imhotep Query Language (IQL)
- 25. IQL - Imhotep Query Language Expressive SQL-like language for aggregate analytics.
- 26. IQL queries - requirements Dataset Date range
- 27. IQL queries - optional Dataset Date range Filters Group by Metrics
- 28. IQL - Dataset from searchresults ‘2013-12-05’ ‘2013-12-10’ where country=ie and jobagedays<1 group by time(1d) select clicked, count() Dataset
- 29. IQL - Date Range from searchresults ‘2013-12-05’ ‘2013-12-10’ where country=ie and jobagedays<1 group by time(1d) select clicked, count() Date Range
- 30. IQL - Filters from searchresults ‘2013-12-05’ ‘2013-12-10’ where country=ie and jobagedays<1 group by time(1d) select clicked, count() Filters
- 31. IQL - Group by from searchresults ‘2013-12-05’ ‘2013-12-10’ where country=ie and jobagedays<1 group by time(1d) select clicked, count() Groups
- 32. IQL - Metrics from searchresults ‘2013-12-05’ ‘2013-12-10’ where country=ie and jobagedays<1 group by time(1d) select clicked, count() Metrics
- 33. IQL - Example Results
- 35. Extract Transform Load (ETL)
- 36. Extract ● Up to your data schema
- 37. Transform ● De-Normalize Data ● Formating
- 38. Example Datasets ● Jobsearch ● Ad Clicks ● Resume Contacts
- 39. Load ● TSV/CSV Uploader
- 40. Load ● TSV/CSV Uploader ● Java API
- 41. Inverted Index ● Massive compression ● Fast Boolean Search
- 42. Opensource Package ● Run on any* computer/s ● Can deploy to AWS using handy AWS CloudFormation script
- 43. CloudFormation Setup ● Create S3 buckets ● Create EC2 Key Pair ● Run CloudFormation script
- 44. DEMO
- 45. Q&A
- 46. Helpful Workshop Links http://go.indeed.com/iws
Editor's Notes
- https://bugs.indeed.com/browse/JASX-4581
- Can combine results from multiple datasets Allows for automation of data tools
- make sure to start with just -"dataset" + time range - view the fields - add a filter field=term - add inequality filter, lucene, regex. - group by time - group by time and user in X - group by buckets (tottime) - add metric (count) - add distinct(user) - add filter as metric (user=zak) - add inequality as a metric (% of queries under 5000ms) - average tottime/count() - percentile(tottime,50)