consistent-hashing | Consistent Hashing in pure Ruby | Hashing library

 by   domnikl Ruby Version: Current License: No License

kandi X-RAY | consistent-hashing Summary

kandi X-RAY | consistent-hashing Summary

consistent-hashing is a Ruby library typically used in Security, Hashing, Example Codes applications. consistent-hashing has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

an implementation of Consistent Hashing in pure Ruby using an AVL tree
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            kandi-support Support

              consistent-hashing has a low active ecosystem.
              It has 40 star(s) with 9 fork(s). There are 4 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 0 open issues and 1 have been closed. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of consistent-hashing is current.

            kandi-Quality Quality

              consistent-hashing has 0 bugs and 0 code smells.

            kandi-Security Security

              consistent-hashing has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              consistent-hashing code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              consistent-hashing does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              OutlinedDot
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              consistent-hashing releases are not available. You will need to build from source code and install.
              Installation instructions, examples and code snippets are available.
              consistent-hashing saves you 277 person hours of effort in developing the same functionality from scratch.
              It has 670 lines of code, 40 functions and 11 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed consistent-hashing and discovered the below as its top functions. This is intended to give you an instant insight into consistent-hashing implemented functionality, and help decide if they suit your requirements.
            • Return the next node .
            • Returns the minimum value .
            • Add a node to this node .
            • Deletes the replica from the node .
            • returns the nearest point for the given key
            • Returns the next node for the given key .
            • Hash of key
            • Returns the length of the ring
            • Set the index .
            • Returns the node for the given key
            Get all kandi verified functions for this library.

            consistent-hashing Key Features

            No Key Features are available at this moment for consistent-hashing.

            consistent-hashing Examples and Code Snippets

            No Code Snippets are available at this moment for consistent-hashing.

            Community Discussions

            QUESTION

            kong-ingress-controller's EXTERNAL_IP is pending
            Asked 2021-Sep-17 at 08:00

            I've installed kong-ingress-controller using yaml file on a 3-nodes k8s cluster( bare metal ) (you can see the file at the bottom of question) and every thing is up and runnig:

            ...

            ANSWER

            Answered 2021-Sep-14 at 12:40

            Had the same issue, after days of looking for a solution, I came across metallb, from nginx ingress installation on bare metal

            MetalLB provides a network load-balancer implementation for Kubernetes clusters that do not run on a supported cloud provider, effectively allowing the usage of LoadBalancer Services within any cluster

            , from their documentation I got this

            Kubernetes does not offer an implementation of network load balancers (Services of type LoadBalancer) for bare-metal clusters. The implementations of network load balancers that Kubernetes does ship with are all glue code that calls out to various IaaS platforms (GCP, AWS, Azure…). If you’re not running on a supported IaaS platform (GCP, AWS, Azure…), LoadBalancers will remain in the “pending” state indefinitely when created.

            I didn't finalize the installation but I hope the explanation above answers your question on pending status on external ip

            Source https://stackoverflow.com/questions/69158477

            QUESTION

            Akka.NET with persistence dropping messages when CPU in under high pressure?
            Asked 2021-Jan-28 at 17:29

            I make some performance testing of my PoC. What I saw is my actor is not receiving all messages that are sent to him and the performance is very low. I sent around 150k messages to my app, and it causes a peak on my processor to reach 100% utilization. But when I stop sending requests 2/3 of messages are not delivered to the actor. Here is a simple metrics from app insights:

            To prove I have almost the same number of event persistent in mongo that my actor received messages.

            Secondly, performance of processing messages is very disappointing. I get around 300 messages per second.

            I know Akka.NET message delivery is at most once by default but I don't get any error saying that message were dropped.

            Here is code: Cluster shard registration:

            ...

            ANSWER

            Answered 2021-Jan-28 at 17:29

            So there are two issues going on here: actor performance and missing messages.

            It's not clear from your writeup, but I'm going to make an assumption: 100% of these messages are going to a single actor.

            Actor Performance

            The end-to-end throughput of a single actor depends on:

            1. The amount of work it takes to route the message to the actor (i.e. through the sharding system, hierarchy, over the network, etc)
            2. The amount of time it takes the actor to process a single message, as this determines the rate at which a mailbox can be emptied; and
            3. Any flow control that affects which messages can be processed when - i.e. if an actor uses stashing and behavior switching, the amount of time an actor spends stashing messages while waiting for its state to change will have a cumulative impact on the end-to-end processing time for all stashed messages.

            You will have poor performance due to item 3 on this list. The design that you are implementing calls Persist and blocks the actor from doing any additional processing until the message is successfully persisted. All other messages sent to the actor are stashed internally until the previous one is successfully persisted.

            Akka.Persistence offers four options for persisting messages from the point of view of a single actor:

            • Persist - highest consistency (no other messages can be processed until persistence is confirmed), lowest performance;
            • PersistAsync - lower consistency, much higher performance. Doesn't wait for the message to be persisted before processing the next message in the mailbox. Allows multiple messages from a single persistent actor to be processed concurrently in-flight - the order in which those events are persisted will be preserved (because they're sent to the internal Akka.Persistence journal IActorRef in that order) but the actor will continue to process additional messages before the persisted ones are confirmed. This means you probably have to modify your actor's in-memory state before you call PersistAsync and not after the fact.
            • PersistAll - high consistency, but batches multiple persistent events at once. Same ordering and control flow semantics as Persist - but you're just persisting an array of messages together.
            • PersistAllAsync - highest performance. Same semantics as PersistAsync but it's an atomic batch of messages in an array being persisted together.

            To get an idea as to how the performance characteristics of Akka.Persistence changes with each of these methods, take a look at the detailed benchmark data the Akka.NET organization has put together around Akka.Persistence.Linq2Db, the new high performance RDBMS Akka.Persistence library: https://github.com/akkadotnet/Akka.Persistence.Linq2Db#performance - it's a difference between 15,000 per second and 250 per second on SQL; the write performance is likely even higher in a system like MongoDB.

            One of the key properties of Akka.Persistence is that it intentionally routes all of the persistence commands through a set of centralized "journal" and "snapshot" actors on each node in a cluster - so messages from multiple persistent actors can be batched together across a small number of concurrent database connections. There are many users running hundreds of thousands of persistent actors simultaneously - if each actor had their own unique connection to the database it would melt even the most robustly vertically scaled database instances on Earth. This connection pooling / sharing is why the individual persistent actors rely on flow control.

            You'll see similar performance using any persistent actor framework (i.e. Orleans, Service Fabric) because they all employ a similar design for the same reasons Akka.NET does.

            To improve your performance, you will need to either batch received messages together and persist them in a group with PersistAll (think of this as de-bouncing) or use asynchronous persistence semantics using PersistAsync.

            You'll also see better aggregate performance if you spread your workload out across many concurrent actors with different entity ids - that way you can benefit from actor concurrency and parallelism.

            Missing Messages

            There could be any number of reasons why this might occur - most often it's going to be the result of:

            1. Actors being terminated (not the same as restarting) and dumping all of their messages into the DeadLetter collection;
            2. Network disruptions resulting in dropped connections - this can happen when nodes are sitting at 100% CPU - messages that are queued for delivery at the time can be dropped; and
            3. The Akka.Persistence journal receiving timeouts back from the database will result in persistent actors terminating themselves due to loss of consistency.

            You should look for the following in your logs:

            • DeadLetter warnings / counts
            • OpenCircuitBreakerExceptions coming from Akka.Persistence

            You'll usually see both of those appear together - I suspect that's what is happening to your system. The other possibility could be Akka.Remote throwing DisassociationExceptions, which I would also look for.

            You can fix the Akka.Remote issues by changing the heartbeat values for the Akka.Cluster failure-detector in configuration https://getakka.net/articles/configuration/akka.cluster.html:

            Source https://stackoverflow.com/questions/65918832

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install consistent-hashing

            [sudo] gem install consistent-hashing

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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