I was checking Memgraph docs and wondering whether Memgraph has replication and why is that feature even useful? Is it for high availability? Does it allow queries to be parallelized? Something else?
In Memgraph, every read query is executed in its own thread, so you get the bonus from running queries in parallel with the respect to your number of cores.
The data is replicated in whole in Memgraph, with 2 replicas. So the engine can actually query either one of those machines. Memgraph is not yet distributed, i.e. it doesn’t shard the data, but keeps the same data in all the replicas. However, distributed Memgraph is currently in the works.
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We have setup a 3 node performance cluster with 16G RAM and 8 Cores each. Our use case is to write 1 million rows to a single table with 101 columns which is currently taking 57-58 mins for the write operation. What should be our first steps towards optimizing the write performance on our cluster?
The first thing I would do is look at the application that is performing the writes:
What language is the application written in and what driver is it using? Some drivers can offer better inherent performance than others. i.e. Python, Ruby, and Node.js drivers may only make use of one thread, so running multiple instances of your application (1 per core) may be something to consider. Your question is tagged 'spark-cassandra-connector' so that possibly indicates your are using that, which uses the datastax java driver, which should perform well as a single instance.
Are your writes asynchronous or are you writing data one at a time? How many writes does it execute concurrently? Too many concurrent writes could cause pressure in Cassandra, but not very many concurrent writes could reduce throughput. If you are using the spark connector are you using saveToCassandra/saveAsCassandraTable or something else?
Are you using batching? If you are, how many rows are you inserting/updating per batch? Too many rows could put a lot of pressure on cassandra. Additionally, are all of your inserts/updates going to the same partition within a batch? If they aren't in the same partition, you should consider batching them up.
Spark Connector Specific: You can tune the write settings, like batch size, batch level (i.e. by partition or by replica set), write throughput in mb per core, etc. You can see all these settings here.
The second thing I would look at is look at metrics on the cassandra side on each individual node.
What does the garbage collection metrics look like? You can enable GC logs by uncommenting lines in conf/cassandra-env.sh (As shown here). Are Your Garbage Collection Logs Speaking to You?. You may need to tune your GC settings, if you are using an 8GB heap the defaults are usually pretty good.
Do your cpu and disk utilization indicate that your systems are under heavy load? Your hardware or configuration could be constraining your capability Selecting hardware for enterprise implementations
Commands like nodetool cfhistograms and nodetool proxyhistograms will help you understand how long your requests are taking (proxyhistograms) and cfhistograms (latencies in particular) could give you insight into any other possibile disparities between how long it takes to process the request vs. perform mutation operations.
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.
I am looking at porting a Java application to .NET, the application currently uses EhCache quite heavily and insists that it wants to support strong consistency (http://ehcache.org/documentation/get-started/consistency-options).
I am would like to use Redis in place of EhCache but does Redis support strong consistency or just support eventual consistency?
I've seen talk of a Redis Cluster but I guess this is a little way off release yet.
Or am I looking at this wrong? If Redis instance sat on a different server altogether and served two frontend servers how big could it get before we'd need to look at a Master / Slave style affair?
A single instance of Redis is consistent. There are options for consistency across many instances. #antirez (Redis developer) recently wrote a blog post, Redis data model and eventual consistency, and recommended Twemproxy for sharding Redis, which would give you consistency over many instances.
I don't know EhCache, so can't comment on whether Redis is a suitable replacement. One potential problem (porting to .NET) with Twemproxy is it seems to only run on Linux.
How big can a single Redis instance get? Depends on how much RAM you have.
How quickly will it get this big? Depends on how your data looks.
That said, in my experience Redis stores data quite efficiently. One app I have holds info for 200k users, 20k articles, all relationships between objects, weekly leader boards, stats, etc. (330k keys in total) in 400mb of RAM.
Redis is easy to use and fun to work with. Try it out and see if it meets your needs. If you do decide to use it and might one day want to shard, shard your data from the beginning.
Redis is not strongly consistent out of the box. You will probably need to apply 3rd party solutions to make it consistent. Here is a quote from docs:
Write safety
Redis Cluster uses asynchronous replication between nodes, and last failover wins implicit merge function. This means that the last elected master dataset eventually replaces all the other replicas. There is always a window of time when it is possible to lose writes during partitions. However these windows are very different in the case of a client that is connected to the majority of masters, and a client that is connected to the minority of masters.
Usually you need to have synchronous replication to achieve strong consistence in a distributed partitioned systems.
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.
I am considering the option of neo4j for some of the new projects I am working for. For the given data needs (inherently graph based) neo4j fits well and a quick prototype is giving good response time for me. What I want to understand is how to scale a neo4j deployment. Specifically:
How do I shard my data across neo4j deployments. Since neo4j is deployed on a single machine, there is a limit to how much data I can store in a single machine and hence I would like to know how to distribute it. Clearly if I split it on users, then relationships between disconnected users (across shards) cannot be maintained.
How do I replicate the neo4j data? I am potentially thinking of putting up a sql-like-setup with masters used for write and slaves used for reads so that we can both scale up our potentially readers and writers, but also have a backup of our data in real time. I understand that all the neo4j data is stored in a filesystem - which is inherently non-replicatable. Is there a way I can do it here? Perhaps, something akin to a mysql bin log?
sharding is as of now not handled by Neo4j itself, but by the domain, much as you describe. Neo4j 2.0 is going to target that problem.
For replication, Online Backup is working and real High Availability with Master failover is in the works, using ZooKeeper to track the cluster nodes and elect new masters, etc.
Any more details on your app sharding requirements? What domain etc?