key-value-store | value store API with implementations for different backends | REST library

 by   webmozart PHP Version: 1.0.0 License: MIT

kandi X-RAY | key-value-store Summary

kandi X-RAY | key-value-store Summary

key-value-store is a PHP library typically used in Web Services, REST, React applications. key-value-store has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

[Dependency Status] A key-value store API with implementations for different backends.

            kandi-support Support

              key-value-store has a low active ecosystem.
              It has 127 star(s) with 10 fork(s). There are 12 watchers for this library.
              It had no major release in the last 12 months.
              There are 1 open issues and 4 have been closed. On average issues are closed in 151 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of key-value-store is 1.0.0

            kandi-Quality Quality

              key-value-store has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              key-value-store is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              key-value-store releases are available to install and integrate.
              Installation instructions, examples and code snippets are available.
              key-value-store saves you 2054 person hours of effort in developing the same functionality from scratch.
              It has 4512 lines of code, 493 functions and 53 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed key-value-store and discovered the below as its top functions. This is intended to give you an instant insight into key-value-store implemented functionality, and help decide if they suit your requirements.
            • Unserialize a value .
            • Unserialize a value .
            • Update meta data .
            • Creates an exception for the given type .
            • Creates an exception for a given value .
            • Validates multiple keys .
            • Create an exception for the given exception .
            • Creates an exception for the given key .
            • Clears the cache .
            • Get client keys .
            Get all kandi verified functions for this library.

            key-value-store Key Features

            No Key Features are available at this moment for key-value-store.

            key-value-store Examples and Code Snippets

            No Code Snippets are available at this moment for key-value-store.

            Community Discussions


            Apache Ignite automatically load cache from 3rd party database (postgresql with sql schema)
            Asked 2021-Sep-15 at 19:06

            I am currently building an application with different ignite configurations. Right now, I am exploring the cache features of Ignite. My goal is to use the Ignite only as a cache. The data should be stored in a 3rd party database (postgres). I use the tpc-h data schema in my database. So, it's not a key-value-store, but ordinary sql.

            Current situation: I have a postgres database (database-name: "db") with a "Nation" table. The table is filled with a few entries (Postgres Table "Nation"). I also have already built up an Ignite-Cluster in my Google-Kubernetes-Engine. I use my own Ignite-Container-Image. It's basically the official one with the postgresql-driver added to the classpath. My ignite-configuration looks like this:



            Answered 2021-Sep-10 at 12:55

            Unfortunately, there is no built-in mechanism to trigger the #loadCache method from outside of the cluster therefore it's required to write some auxiliary code. But, once it's written you might just wrap it into s compute task and invoke it using, say, REST API.



            How to store random integers from the set of 32-bit integers in a compact data structure to rapidly check membership?
            Asked 2021-Jan-10 at 20:01

            I am thinking about how to organize/allocate memory and that led me to this question, which I have distilled to its essence, so it may seem out of the blue, but the memory question is too complicated and confusing and distracting, so I am not asking it here. I tried asking it earlier and got no response. So here it goes.

            Say you have a system where you want to check if you have some integer exists in it as fast as possible, and you want to be able to add and remove integers as fast as possible too. That is, it's a key-store essentially, not even a key-value-store. It only stores integer keys, and I guess theoretically the value would conceptually be a true boolean, but I don't think it needs to be there necessarily.

            One solution is to use sparse arrays. For example, this array would return true in O(1) time for 3, 5, and 9.



            Answered 2021-Jan-10 at 19:41

            Here is my suggested approach.

            Every node should be a 32 entry array. If the first entry is null or an array, it is a 32-way split of the whole search space. Otherwise it is descending list of the entries in this block. Fill in the non-entries with -1.

            This means that in no more than 5 lookups we get to the block that either has or doesn't have our value. We then do a linear scan. A binary search of a sorted list naively seems like it would be more efficient, but in fact it involves a series of hard to predict branches which CPUs hate. In a low level language (which I hope yours is), it is faster to avoid the pipeline stall and do a linear search.

            Here is an implementation in JavaScript.



            Accessing Array elements in array of Objects - JavaScript
            Asked 2020-Nov-07 at 20:55

            So I am trying to get elements from JSON of Objects Example JSON Data:



            Answered 2020-Nov-07 at 20:55

            You don't need to use .get:



            Performance comparison of using Redis hashes vs many keys
            Asked 2020-Jul-19 at 09:38

            Okay, I'm currently planning on using Redis as a front end cache to my NoSQL database. I will be storing a lot of frequently used user data in the Redis database. I was wondering if making a key-value entry for each user would be better or using the Redis hash where the field is the user id and the value is a large json object. What do you think would be better?

            I saw this article to sort of answer the question, but it doesn't discuss the limitations on value size.



            Answered 2020-Jul-18 at 22:46

            Choosing hash over string has many benefits and some drawbacks depending on the use cases. If you are going to choose hash, it is better to design your json object as hash fields & values such as;



            How would I parse JSON in Ruby to give me specific output
            Asked 2020-Jul-15 at 14:20

            So I'm trying to make in Ruby so that it "Idk how to express myself" parse JSON from this API so the output is:



            Answered 2020-Jul-15 at 14:14

            Sure, you do it like this:



            Creating compile-time Key-Value map in C++
            Asked 2020-Apr-19 at 22:23

            I have tried to create a compile-time simple Key-Value map in C++. I'm compiling with /std:c++11. (Using IAR compiler for embedded code and only cpp++11 is supported at the moment)

            I've learnt a little bit about meta-programming.

            I don't want my map to have a default value, if key is not found, like this post: How to build a compile-time key/value store?

            I want to get compiler error, if in my code I'm trying to get a value which is not stored in the map.

            Here is what I've done:



            Answered 2020-Apr-19 at 22:23

            Don't write a template metaprogram, where it is not necessary. Try this simple solution (CTMap stands for compile time map):



            Do time complexities differ between list and deque for Bisect/Insort?
            Asked 2020-Feb-26 at 01:38

            As far as I know, list in Python is implemented using array, while deque is implemented using double linked list. In either case, the binary search of a certain value takes O(logn) time, but if we insert to that position, array takes O(n) while double linked list takes O(1).

            So, can we use the combination of bisect, insort, and deque to implement all Dynamic Set Operations with time complexity comparable to TreeMap in Java?

            Update: I tested it in this Leetcode question:

            Quite the contrary to my expectation, when I switch from list to deque, the speed slowed down a lot.



            Answered 2020-Feb-26 at 01:38

            To your title question: Yes, they do.

            To your hypothetical sorted set implementation question: No, you can't.

            One, you're mistaken on the implementation of deque; it's not a plain "item per node" linked list, it's a block of items per node (64 on the CPython reference interpreter, though that's an implementation detail). And aside from the head and tail blocks, internal blocks are never left empty, so insertion midway through the deque isn't particularly cheap, it still has to move a bunch of stuff around. It's not O(n) like a mid-list insertion, as it takes advantage of some efficiencies in rotation to rotate, append to one side or the other, then rotate back, but it's a far cry from insertion at a known point in a linked list, remaining O(n) (though with large constant divisors thanks to shuffling whole blocks being cheaper than moving each of the individual items).

            Two, each lookup in a deque is O(n), not O(1) like a list; it has a constant divisor of 64 as stated previously, and it's drops to O(1) near either end of the deque, but it's still O(n) in general, which scales poorly for large deques. bisect searches are O(log n) under the assumption that indexing the sequence is O(1); for a deque, they'd be O(n log n), as they'd perform log n O(n) indexing operations. This matches your results from testing; bisect+deque is significantly worse.

            Java's TreeMap isn't implemented in terms of binary search and a linked list in any event; linked lists are no good for this, since ultimately a full binary search must traverse back and forth enough that it does O(n) total work, even if it only has to compare against O(log n) elements. A tree map needs a tree structure of some sort, you can't just fake it with a linked list and a good algorithm.

            Built-in alternatives include:

            1. insort of a normal list: Sure it's O(n) overall, but the expensive part (finding where to insert) is O(log n), and it's only the "make room" step that's O(n), and it's a really cheap O(n) (basically a memcpy). Not acceptable for truly huge lists, but you'd be surprised how large a list you'd need before the overhead was noticeable against Python's slowness.
            2. Delayed, buffered sorting: If lookups are infrequent, but insertions are common, defer the sort until needed to minimize the number of sorting operations; just append the new elements to the end without sorting and set a "needs sorting" flag, and re-sort before a lookup when the flag is set. The TimSort algorithm does very well when the input is already mostly sorted (much closer to O(n) than a general purpose sort without optimizations for partially sorted typically can do), so it may be fine.
            3. If you only need the smallest element at any given time, the heapq module can do that with true O(log n) insertions and removals, and gets the minimum with O(1) (it's always index 0).
            4. Use a sqlite3 database (possible via shelve), indexed as appropriate; sqlite3 indices default to using a B-tree, meaning queries ordered using the index key(s) get the results back in sorted order "for free".

            Otherwise, you'll have to install a third-party module that provides a proper sorted set-like type.



            Secondary in-memory index representations in Python
            Asked 2020-Feb-02 at 09:30

            I am searching for an efficient solution to build a secondary in-memory index in Python using a high-level optimised mathematical package such as numpy and arrow. I am excluding pandas for performance reasons.


            "A secondary index contains an entry for each existing value of the attribute to be indexed. This entry can be seen as a key/value pair with the attribute value as key and as value a list of pointers to all records in the base table that have this value." - JV. D'Silva et al. (2017)

            Let's take a simple example, we can scale this later on to produce some benchmarks:



            Answered 2020-Feb-02 at 09:30

            I have searched both in the past and in the present for an open-source solution to this problem but I have not found one that satisfies my appetite. This time I decided to start building my own and discuss openly its implementation that also covers the null case, i.e. missing data scenario.

            Do notice that secondary index is very close to adjacency list representation, a core element in my TRIADB project and that is the main reason behind searching for a solution.

            Let's start with one line code using numpy


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


            No vulnerabilities reported

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