Currently I am using PostGIS to query SRTM elevation data, but I am finding that the database is extremely slow (it can take up to 100ms for a single elevation query!). I've highlighted my querying results in Slow Elevation Batch Queries in PostGIS? in the GIS stackexchange. As a result I am looking at switching to Redis cluster to store all of my SRTM elevation data in-memory. The SRTM data has billions of points for the entire earth, and I am curious how Redis would perform under these circumstances. Is Redis even the right tool? Should I look into grouping my points into sets to reduce the number of key/value pairs? Or Should I look into something like MongoDB? Performance and scalability are my main concerns.
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My doubt is,
I have the same instance of Redis, with multiple databases (one for each service).
If more than one service used the same database, would the prefix search be slower? (having the data of all the services in one place and having to go through all of them, as opposed to only going through the selected base)
Partitioning in Redis serves two main goals:
It allows for much larger databases, using the sum of the memory of
many computers. Without partitioning you are limited to the amount of
memory a single computer can support.
It allows scaling the computational power to multiple cores and multiple computers, and the network bandwidth to multiple computers and network adapters.
Ref
Integration through database should be avoided, read more here: https://martinfowler.com/bliki/IntegrationDatabase.html
If you use KEYS command for it, then read this from documentation:
Warning: consider KEYS as a command that should only be used in production environments with extreme care. It may ruin performance when it is executed against large databases. This command is intended for debugging and special operations, such as changing your keyspace layout. Don't use KEYS in your regular application code. If you're looking for a way to find keys in a subset of your keyspace, consider using SCAN or sets.
Redis doesn't support prefix index, but you can use sorted set to do prefix search to some degree.
Redis is in-memory store, so all reads are pretty fast as long as you model it the right way.
Better query multiple times than doing integration through db...
Btw if you have multiple services owning the same data, then you should probably model your services differently...
In general, I would avoid key prefix searching. REDIS isn't a standard database and key searches are slow.
Since REDIS is a key/value store, it's optimized as such.
To take advantage of REDIS you want to hit the desired key directly.
I expect key search time increases with the total amount of keys, so splitting the database would potentially reduce the key search time.
However, if your doing key searches, I would put the desired keys into a key list and just do a direct look up there.
desired_prefix = ["desired_prefix-a", "desired_prefix-b", ...]
lpush "prefix_x_keys" "x-a"
If you run multiple databases on a single instance, they all will try to use and acquire the same resource and memory plus they will also run their core process which will not help you.
you can think like this, 2 OS running on the same machine will never match the performance of a single OS utilizing all resources of the machine.
What you can do to increase the performance is make more tables or use the partition concept. This will not put too much data in a single table and the search will work faster.
I am working on a cloud application that requires low latency and very high read/writes per second. I will only have around 1 million records stored persistently but this may fluctuate largely as the application runs.
After YCSB benchmarking Aerospike and Redis, I found that Aerospike beats Redis and MongoDB both in terms of performance on a single-node for 60/40 read write.
Some points to note:
Fetching all my data using a single 32-bit integer key (no advanced queries)
Running on a single machine with 8 GB RAM and an SSD (small number of records)
Multiple clients need access to the database at once (via LAN)
I'm also assuming that key-value stores will outperform document stores and are the best fit considering I do not need advanced queries.
Before committing myself to Aerospike, are there any other solutions which may be more fit for my scenario considering that I am only running a single node with a small-ish amount of records?
Not that I'm aware of. I think Aerospike is the fastest.
However, for some use cases you can consider Tarantool.
Here's one of the benchmarks: https://medium.com/#rvncerr/tarantool-vs-competitors-racing-in-microsoft-azure-ebde9c5d619
We are currently using Redis and it's a great in-memory datastore. We're starting to look at some new problems where the in-memory limitation is a factor and looking at other option. One we came across is Aerospike - it seems very fast, even faster than redis on in-memory single-shard operation.
Now that we're adding this to our stack, I'm trying to understand the use cases where Aerospike would not be able to replace redis?
Aerospike supports less data types than Redis, for example pub/sub is not available in Aerospike. However, Aerospike is a distributed key-value store and has superior clustering features.
The two are both great databases. It really depends on how big of a dataset you're handling, and your expectations of growth.
Redis:
Key/value store, dataset fits into RAM in single machine or you can shard yourself across multiple machines (and/or cores since it's single-threaded), persists data to disk, has data structures like lists/sets, basic pub/sub, simple slave replication, Lua scripting.
Aerospike:
Key/value row-store (meaning value contains bins with values and those values can be more maps/lists/values to have multiple levels), multithreaded to use all cores, built for clustering across machines with replication, and can do cross-datacenter replication, Lua scripting for UDFs. Can run directly on SSDs so you can store much more data without it fitting into RAM.
Comparison:
If you just have a smaller dataset or are fine with single-core performance then Redis is great. Quick to install, simple to run, easy to just attach a slave with 1 command if you need more read scalability. Redis also has more unique functionality with list/set/bitmap operations so you can do "more" out of the box.
If you want to store more complicated or nested data or need more performance on a single machine or clustering, then Aerospike gets the job done really well with less operational overhead. Very fast performance and easy cluster setup with all nodes being exactly the same role so you can scale reads and writes.
That's the big difference, scalability beyond a single core or server. With Lua scripting, you can usually fill in any native feature that Redis has into Aerospike. If you have lots of data (like TBs) then Aerospike's SSD feature means you get RAM-like performance without the RAM cost.
Have you looked at the benchmarks? I believe each one performs differently under different conditions and use cases:
http://www.aerospike.com/when-to-use-aerospike-vs-redis/
https://redislabs.com/blog/nosql-performance-aerospike-cassandra-datastax-couchbase-redis
Redis and Aerospike are different and both have their pros and cons, but Redis seems a better fit than Aerospike in the 2 following use cases:
when we don't need replication
We are using a big cache with intensive writes and a very short ttl (20s) for deduplication. There is no point in replicating this data. Redis would probably use half as much cpu and less than half as much RAM than Aerospike. It would be cheaper and as fast, or even faster thanks to pipelining.
when we need cross data-center replication
We have one large database that we need to access from 5 data centres, lots of writes, intensive reads. There is no perfect solution but the best one so far seems to store the central database in Redis and a copy on each data centre using Redis master-slave replication.
We are building cv scoring service, and we are using Postgres for making complex queries to find cv's that match vacancy best.
The problem is, that we use really complex set of heuristics to score cv to vacancy, and the average number of cvs to be scored per query is growing.
I want to put this kind of load outside of database, and looking for existing solutions for horizontal scaling such load.
Query should be executed in fraction of a second, there can be hundreds of concurrent queries. Each query scores on average 10k cvs. Each cv is like about 50 records in maybe 10 tables in its current relational form.
I want a clustered system to run each query in multiple parallel processes (on many servers) and return aggregated result. It should be fast and fault tolerant.
I was looking to Hadoop, but it looks like it is designed for batch processing, and not for realtime low latency load. There is Apache Storm, but it is designed for continous stream processing. So I am not shure :)
What kind of tool could will suit my needs?
Thank you!
Make sure you are not redoing work, if a cv has been scored tag it as scored and don't reprocess unless it's necessary.
Unless you are partitioning the data in postgres you might want to do that. Usually not all rows need to be accessed regularly.
Sounds like you want to primarily scale reads, in that case a postgres read-only cluster could be an option.
Take a look at Elasticsearch, it is designed to do weighted scoring, faceting, etc. It should also scale, haven't tried that myself though.
I would definitely start with 1 though, don't do work unless you have to.
I want to use Redis as a database, not a cache. From my (limited) understanding, Redis is an in-memory datastore. What are the risks of using Redis, and how can I mitigate them?
You can use Redis as an authoritative store in a number of different ways:
Turn on AOF (Append-only File store) see AOF docs. This will keep a log of all Redis commands made against your dataset in real-time.
Run Redis using Master-Slave replication see replication docs. This will allow you to provide high-availability if one of your instances fails.
If you're running on something like EC2 you can EBS back your Redis partition to provide another layer of protection against instance failure.
On the horizon is Redis Cluster - this is specifically designed as a way to run Redis in a way that should help with HA and scalability. However, this won't appear for at least another six months or so.
Redis is an in-memory store which can also write the data back to disc. You can specify how many times to do a fsync to make redis safer(but also slower => trade-off) .
But still I am not certain if redis is in state yet to really store (mission) critical data in it (yet?). If for example it is not a huge problem when 1 more tweets(twitter.com) or something similiar get losts then I would certainly use redis. There is also a lot of information available about persistence at redis's own website.
You should also be aware of some persistence problems which could occur by reading antirez(redis maintainers) blog article. You should read his blog because he has some interesting articles.
I would like to share a few things that we have learned by using Redis as a primary Database in our service. We choose Redis since we had data that could not be partitioned. We wanted to get the best performance we could get out of one box
Pros:
Redis was unbeatable in raw performance. We got 10K transactions per second out of the box (Note that one transaction involved multiple Redis commands). We were able to hit a rate of 25K+ transactions per second after a few optimizations, along with LUA scripts. So when it comes to performance per box, Redis is unmatched.
Redis is very simple to setup and has a very small learning curve as opposed to other SQL and NoSQL datastores.
Cons:
Redis supports only few primitive Data Structures like Hashes, Sets, Lists etc. and operations on these Data Structures. These are more than sufficient when you are using Redis as a cache, but if you want to use Redis as a full fledged primary data store, you will feel constrained. We had a tough time modelling our data requirements using these simple types.
The biggest problem we have seen with Redis was the lack of flexibility. Once you have solutioned the structure of your data, any modifications to storage requirements or access patterns virtually requires re-thinking of the entire solution. Not sure if this is the case with all NoSQL data stores though (I have heard MongoDB is more flexible, but haven't used it myself)
Since Redis is single threaded, CPU utilization is very low. You can't put multiple Redis instances on the same machine to improve CPU utilization as they will compete for the same disk, making disk as the bottleneck.
Lack of horizontal scalability is a problem as mentioned by other answers.
As Redis is an in-memory storage, you cannot store large data that won't fit you machine's memory size. Redis usually work very bad when the data it stores is larger than 1/3 of the RAM size. So, this is the fatal limitation of using Redis as a database.
Certainly, you can distribute you big data into several Redis instances, but you have to do it all on your own manually. The operation usually be done like this(assuming you have only 1 instance from start):
Use its master-slave mechanism to replicate data to the second machine, Now you have 2 copies of the same data.
Cut off the connection between master and slave.
Delete the first half(split by hashing, etc) of data on the first machine, and delete the second half of data on the second machine.
Tell all clients(PHP, C, etc...) to operate on the first machine if the specified keys are on that machine, otherwise operate on the second machine.
This is the way how Redis scales! You also have to stop your service to prevent any writes during the migration.
To the expierence we encounter, we have this conclusion to Redis: Redis is not the right choice to store more than 30G data, Redis is not scalable, Redis is quite suitable for prototype development.
We later find an alternative to Redis, that is SSDB(https://github.com/ideawu/ssdb), a leveldb server that supports nearly all the APIs of Redis, it is suitable for storing more than 1TB of data, that only depends on the size of you harddisk.
Redis is a database, that means we can use it for persisting information for any kind of app, information like user accounts, blog posts, comments and so on. After storing information we can retrieve it later on by writing queries.
Now this behavior is similar to just about every other database, but what is the difference? Or rather why would we use it over any other database?
Redis is fast.
Redis is not fast because it's written in a special programming language or anything like that, it's fast because all data is stored in-memory.
Most databases store all their information between both the memory of a computer and the hard drive. Accessing data in-memory is fast, but getting it stored on a hard disk is relatively slow.
So rather than storing memory in hard disk, Redis decided to store it in memory.
Now, the downside to this is that working with data that is larger than the amount of memory your computer has, that is not going to work.
That may sound like a tremendous problem, but Redis has clear strategies for working around this limitation.
The above is just the first reason why Redis is so fast.
The second reason is that Redis stores all of its data or rather organizes all of its data in simple data structures such as Doubly Linked Lists, Sorted Sets and so on.
These data structures have well-known and well-understood performance characteristics. So as developers we can decide exactly how our information is organized and how to efficiently query data.
It's also very fast because Redis is simple in nature, it's not feature heavy; feature heavy datastores like Postgres have performance penalties.
So to use Redis as a database you have to know how to store in limited space, you have to know how to organize it into these simple data structures mentioned above and you have to understand how to work around the limited feature set.
So as far as mitigating risks, the way you start to do that is to start to think Redis Design Methodology and not SQL Database Design Methodology. What do I mean?
So instead of, step 1. Put the data in tables, step 2. figure out how we will query it.
With Redis it's more:
Step 1. Figure out what queries we need to answer.
Step 2. Structure data to best answer those queries.