kandi X-RAY | key-value-store Summary
kandi X-RAY | key-value-store Summary
[Dependency Status] A key-value store API with implementations for different backends.
Top functions reviewed by kandi - BETA
- 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 .
key-value-store Key Features
key-value-store Examples and Code Snippets
Trending Discussions on key-value-store
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:...
ANSWERAnswered 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.
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....
ANSWERAnswered 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.
So I am trying to get elements from JSON of Objects Example JSON Data:...
ANSWERAnswered 2020-Nov-07 at 20:55
You don't need to use
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....
ANSWERAnswered 2020-Jul-18 at 22:46
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;
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:...
ANSWERAnswered 2020-Jul-15 at 14:14
Sure, you do it like this:
I have tried to create a compile-time simple Key-Value map in C++. I'm compiling with
(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:...
ANSWERAnswered 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):
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
deque to implement all Dynamic Set Operations with time complexity comparable to TreeMap in Java?
Update: I tested it in this Leetcode question: https://leetcode.com/problems/time-based-key-value-store/submissions/
Quite the contrary to my expectation, when I switch from
deque, the speed slowed down a lot.
ANSWERAnswered 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
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
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
O(n) indexing operations. This matches your results from testing;
deque is significantly worse.
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:
insortof 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
memcpy). Not acceptable for truly huge
lists, but you'd be surprised how large a
listyou'd need before the overhead was noticeable against Python's slowness.
- 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-
sortbefore 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.
- If you only need the smallest element at any given time, the
heapqmodule can do that with true
O(log n)insertions and removals, and gets the minimum with
O(1)(it's always index 0).
- Use a
sqlite3database (possible via
shelve), indexed as appropriate;
sqlite3indices 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
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.Definition
"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:...
ANSWERAnswered 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
No vulnerabilities reported
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