fatcache | two ways to think of SSDs in system design | Key Value Database library
kandi X-RAY | fatcache Summary
kandi X-RAY | fatcache Summary
There are two ways to think of SSDs in system design. One is to think of SSD as an extension of disk, where it plays the role of making disks fast and the other is to think of them as an extension of memory, where it plays the role of making memory fat. The latter makes sense when persistence (non-volatility) is unnecessary and data is accessed over the network. Even though memory is thousand times faster than SSD, network connected SSD-backed memory makes sense, if we design the system in a way that network latencies dominate over the SSD latencies by a large factor. To understand why network connected SSD makes sense, it is important to understand the role distributed memory plays in large-scale web architecture. In recent years, terabyte-scale, distributed, in-memory caches have become a fundamental building block of any web architecture. In-memory indexes, hash tables, key-value stores and caches are increasingly incorporated for scaling throughput and reducing latency of persistent storage systems. However, power consumption, operational complexity and single node DRAM cost make horizontally scaling this architecture challenging. The current cost of DRAM per server increases dramatically beyond approximately 150 GB, and power cost scales similarly as DRAM density increases. Fatcache extends a volatile, in-memory cache by incorporating SSD-backed storage. SSD-backed memory presents a viable alternative for applications with large workloads that need to maintain high hit rate for high performance. SSDs have higher capacity per dollar and lower power consumption per byte, without degrading random read latency beyond network latency.
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QUESTION
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 17:02Short 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:
- Select only data you need, not entire models.
- 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).
- Consider using the
toBase()method. This retrieves all data but does not create the Eloquent model, saving precious resources. - 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.
QUESTION
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 01:12Compacted 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:
- Do stateful stream processing at scale with Kafka Streams
- 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.
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