pmemkv | Key/Value Datastore for Persistent Memory | Key Value Database library

 by   pmem C++ Version: 1.4.1 License: Non-SPDX

kandi X-RAY | pmemkv Summary

kandi X-RAY | pmemkv Summary

pmemkv is a C++ library typically used in Database, Key Value Database applications. pmemkv has no bugs, it has no vulnerabilities and it has low support. However pmemkv has a Non-SPDX License. You can download it from GitHub.

pmemkv is a local/embedded key-value datastore optimized for persistent memory. Rather than being tied to a single language or backing implementation, pmemkv provides different options for language bindings and storage engines.
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            kandi-support Support

              pmemkv has a low active ecosystem.
              It has 387 star(s) with 119 fork(s). There are 33 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 52 open issues and 261 have been closed. On average issues are closed in 531 days. There are 3 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of pmemkv is 1.4.1

            kandi-Quality Quality

              pmemkv has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              pmemkv has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              pmemkv releases are available to install and integrate.
              Installation instructions are available. Examples and code snippets are not available.

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            pmemkv Key Features

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            pmemkv Examples and Code Snippets

            No Code Snippets are available at this moment for pmemkv.

            Community Discussions

            QUESTION

            Laravel how to "properly" store & retrieve models in a Redis hash
            Asked 2021-Jul-08 at 17:02

            I'm developing a Laravel application & started using Redis as a caching system. I'm thinking of caching the data of all of a specific model I have, as a user may make an API request that this model is involved in quite often. Would a valid solution be storing each model in a hash, where the field is that record's unique ID, and the values are just the unique model's data, or is this use case too complicated for a simple key value database like Redis? I"m also curious as to how I would create model instances from the hash, when I retrieve all the data from it. Replies are appreciated!

            ...

            ANSWER

            Answered 2021-Jul-08 at 17:02

            Short answer: Yes, you can store a model, or collections, or basically anything in the key-value caching of Redis. As long as the key provided is unique and can be retraced. Redis could even be used as a primary database.

            Long answer

            Ultimately, I think it depends on the implementation. There is a lot of optimization that can be done before someone can/should consider caching all models. For "simple" records that involve large datasets, I would advise to first optimize your queries and code and check the results. Examples:

            1. Select only data you need, not entire models.
            2. Use the Database Query Builder for interacting with the database when targeting large records, rather than Eloquent (Eloquent is significantly slower due to the Active Record pattern).
            3. Consider using the toBase() method. This retrieves all data but does not create the Eloquent model, saving precious resources.
            4. Use tools like the Laravel debugbar to analyze and discover potential long query loads.

            For large datasets that do not change often or optimization is not possible anymore: caching is the way to go!

            There is no right answer here, but maybe this helps you on your way! There are plenty of packages that implement similar behaviour.

            Source https://stackoverflow.com/questions/68305332

            QUESTION

            Can compacted Kafka topic be used as key-value database?
            Asked 2020-Nov-25 at 01:12

            In many articles, I've read that compacted Kafka topics can be used as a database. However, when looking at the Kafka API, I cannot find methods that allow me to query a topic for a value based on a key.

            So, can a compacted Kafka topic be used as a (high performance, read-only) key-value database?

            In my architecture I want to feed a component with a compacted topic. And I'm wondering whether that component needs to have a replica of that topic in its local database, or whether it can use that compacted topic as a key value database instead.

            ...

            ANSWER

            Answered 2020-Nov-25 at 01:12

            Compacted kafka topics themselves and basic Consumer/Producer kafka APIs are not suitable for a key-value database. They are, however, widely used as a backstore to persist KV Database/Cache data, i.e: in a write-through approach for instance. If you need to re-warmup your Cache for some reason, just replay the entire topic to repopulate.

            In the Kafka world you have the Kafka Streams API which allows you to expose the state of your application, i.e: for your KV use case it could be the latest state of an order, by the means of queriable state stores. A state store is an abstraction of a KV Database and are actually implemented using a fast KV database called RocksDB which, in case of disaster, are fully recoverable because it's full data is persisted in a kafka topic, so it's quite resilient as to be a source of the data for your use case.

            Imagine that this is your Kafka Streams Application architecture:

            To be able to query these Kafka Streams state stores you need to bundle an HTTP Server and REST API in your Kafka Streams applications to query its local or remote state store (Kafka distributes/shards data across multiple partitions in a topic to enable parallel processing and high availability, and so does Kafka Streams). Because Kafka Streams API provides the metadata for you to know in which instance the key resides, you can surely query any instance and, if the key exists, a response can be returned regardless of the instance where the key lives.

            With this approach, you can kill two birds in a shot:

            1. Do stateful stream processing at scale with Kafka Streams
            2. Expose its state to external clients in a KV Database query pattern style

            All in a real-time, highly performant, distributed and resilient architecture.

            The images were sourced from a wider article by Robert Schmid where you can find additional details and a prototype to implement queriable state stores with Kafka Streams.

            Notable mention:

            If you are not in the mood to implement all of this using the Kafka Streams API, take a look at ksqlDB from Confluent which provides an even higher level abstraction on top of Kafka Streams just using a cool and simple SQL dialect to achieve the same sort of use case using pull queries. If you want to prototype something really quickly, take a look at this answer by Robin Moffatt or even this blog post to get a grip on its simplicity.

            While ksqlDB is not part of the Apache Kafka project, it's open-source, free and is built on top of the Kafka Streams API.

            Source https://stackoverflow.com/questions/64996101

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

            Vulnerabilities

            No vulnerabilities reported

            Install pmemkv

            Installation guide provides detailed instructions how to build and install pmemkv from sources, build rpm and deb packages and explains usage of experimental engines and pool sets.
            Building from Sources
            Installing on Fedora
            Installing on Ubuntu
            Using Experimental Engines
            Building Packages
            Using a Pool Set

            Support

            For more information about pmemkv, contact Igor Chorążewicz (igor.chorazewicz@intel.com), Piotr Balcer (piotr.balcer@intel.com) or post on our #pmem Slack channel using this invite link or Google group.
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