Raft is a distributed consensus algorithm. Here is a simple way to understand what problem such algorithms and Raft in particular solves: Since it’s already summer, imagine there is an ice-cream delivery server which manages order info of various customers. There are many reasons the ice-cream company may want to run multiple instances of this server, one of them being availability i.e. even if one crashes, you can still order your ice-cream. But once we have multiple instances, consistency can take a hit. What if when your ice-cream is about to be dispatched, one server says you asked for mango flavor, but another says you asked for orange! (Maybe in reality, you asked for orange, but later changed to mango and the second server is just lagging) This is what distributed (i.e. multiple ice cream servers) consensus (i.e agree on the same flavor) is. One popular way to solve this problem is with a leader-follower server approach i.e. for consistency (strong, not eventual) one can always query the leader which is supposed to have the most updated state. In the background, the leader replicates its state to all the followers, so that one of them can become leader in case the current leader crashes. Raft, among other things, provides a leader election algorithm. Interesting thing about Raft is that it was designed for “understandability” as a metric. The consensus algorithm before Raft, “Paxos” , was notoriously difficult to understand, limiting the number of its practical implementations and rate of improvement. I am trying to understand the Raft algorithm myself, and wouldn’t attempt simplifying its details just yet. In case this sounds interesting to you, you may want to check out the original Raft paper linked in comments! #programming?#softwareengineering?#computerscience?#computernetworks?#Database?#DistributedSystems #Raft
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#ErrorDescription - library for MetaTrader 5 https://lnkd.in/e2nuG85N # The library contains the following functions: TradeServerReturnCodeDescription - returns description of trade server return codes; ErrorDescription - returns description of runtime errors. //+-------------------------------------------...
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For all my driven developers, GenAI enthusiasts, and push the envelope innovators...I know you're swamped - but hungry...here's a free on-demand (1hr) course to uplevel fast, led by MongoDB Developer Advocate Richmond Alake! Our new course on DeepLearning.AI dives into integrating traditional database features with vector search capabilities to optimize large-scale Retrieval Augmented Generation (RAG) applications. ?? https://lnkd.in/dCEP_6N5
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Good morning!! Some updates with TidesDB. Finally added range and filter methods. You can now use tidesdb_range with a start and end key which will return a range of key value pairs found in a column family. You can also use tidesdb_filter and pass a filter method and gather a result set of key value pairs based on your filter. VERY COOL!! https://lnkd.in/eEX7i54x #tidesdb #lsmtree #opensource #clib #algorithms #keyvalue #storageengine
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Back when I started exploring distributed systems & system design, I couldn’t find any cheat sheet on tradeoffs. No resource gave me all the info in one place, so I decided to make one myself. Here are the links to my learnings: ????????????????? ????. ??????????????: https://lnkd.in/gGyTb3Jh ????????????????????? ????. ??????????????: https://lnkd.in/gzNb85xY ????????? ????. ???????? ????????????????????????: https://lnkd.in/gKaaG-d6 ????????????? ???? ??????????????: https://lnkd.in/gGJ_SSDT ??????? ???? ?????????? ??????????????????: https://lnkd.in/g6QCyZga – P.S: I write a lot about distributed systems and system design, let me know if there’s anything you’d like me to cover…
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Because of the decentralized nature of Dolt, we prefer UUID keys over AUTO_INCREMENT keys. We have a new feature called last_insert_uuid() that makes using UUID keys easier. https://lnkd.in/gB_zhyQZ
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UUID v4 is a good standard, but could it be better? This well written blog points out some of the pros and cons for using V7. https://lnkd.in/dhXweJVU
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?? New tutorial on our new Proxy class for caching remote arrays ?? https://lnkd.in/d-dfju5W Advantages: 1) Granularity: when slicing, only the necessary chunks are downloaded 2) Automatic caching: no more re-downloads on data visited before 3) Compression is everywhere: fetching, transmission and local storage To set up the proxy instance, you only need one additional step. After that, you can access a remote, compressed, n-dimensional dataset as if it were local to your machine, leveraging the speed and efficiency of the Blosc2 library. Compress better, share faster
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#Authenticate into an MT4 account from MQL4 (tested in build 880) - library MetaTrader 4 https://lnkd.in/ehjvBfvh # Since MetaTrader 4 build 880 was released, it's no longer easy to programmatically fill values in Login dialog. This library hides the complicated stuff and provides easy to use API to login to any MetaTrader 4 account from within MQL4 script/expert. Any contribut...
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11 个月Ref: https://raft.github.io/raft.pdf