We keep continuously writing and updating events into redis and so when we ever we want to read data(which is a lot of data , upwards of for 500000 key value pairs), redis has performance issues. So, we decided to get the data via multiple threads. But because of single instance redis , the performance issues persisted .Will replication help us? As in, by making master and slave redis's , will our reads of the events be distributed to the slaves . We are thinking of making the master write only.
Any other suggestion for performance improvements?
(one of) Replication's declared purposes is to help in scaling reads, so yes to the topic.
Note that after you've set up the slave, you'll need to specify its address for your reader threads and processes. Make sure that you start with read-slaves if you don't have a clear separation between writers and readers.
If a single slave isn't enough, you can actually add more slaves. If you add them directly to the master, you'll get fresher reads but there'll eventually be a performance impact on the master. Alternatively, replication chaining is a great solution for most use cases, i.e. 1 master -> 1 slave -> n slaves.
There are probably other ways to scale Redis for your use case (e.g. clustering), but that really depends on what you're trying/wanting to do :)
Related
I've found different zookeeper definitions across multiple resources. Maybe some of them are taken out of context, but look at them pls:
A canonical example of Zookeeper usage is distributed-memory computation...
ZooKeeper is an open source Apache™ project that provides a centralized infrastructure and services that enable synchronization across a cluster.
Apache ZooKeeper is an open source file application program interface (API) that allows distributed processes in large systems to synchronize with each other so that all clients making requests receive consistent data.
I've worked with Redis and Hazelcast, that would be easier for me to understand Zookeeper by comparing it with them.
Could you please compare Zookeeper with in-memory-data-grids and Redis?
If distributed-memory computation, how does zookeeper differ from in-memory-data-grids?
If synchronization across cluster, than how does it differs from all other in-memory storages? The same in-memory-data-grids also provide cluster-wide locks. Redis also has some kind of transactions.
If it's only about in-memory consistent data, than there are other alternatives. Imdg allow you to achieve the same, don't they?
https://zookeeper.apache.org/doc/current/zookeeperOver.html
By default, Zookeeper replicates all your data to every node and lets clients watch the data for changes. Changes are sent very quickly (within a bounded amount of time) to clients. You can also create "ephemeral nodes", which are deleted within a specified time if a client disconnects. ZooKeeper is highly optimized for reads, while writes are very slow (since they generally are sent to every client as soon as the write takes place). Finally, the maximum size of a "file" (znode) in Zookeeper is 1MB, but typically they'll be single strings.
Taken together, this means that zookeeper is not meant to store for much data, and definitely not a cache. Instead, it's for managing heartbeats/knowing what servers are online, storing/updating configuration, and possibly message passing (though if you have large #s of messages or high throughput demands, something like RabbitMQ will be much better for this task).
Basically, ZooKeeper (and Curator, which is built on it) helps in handling the mechanics of clustering -- heartbeats, distributing updates/configuration, distributed locks, etc.
It's not really comparable to Redis, but for the specific questions...
It doesn't support any computation and for most data sets, won't be able to store the data with any performance.
It's replicated to all nodes in the cluster (there's nothing like Redis clustering where the data can be distributed). All messages are processed atomically in full and are sequenced, so there's no real transactions. It can be USED to implement cluster-wide locks for your services (it's very good at that in fact), and tehre are a lot of locking primitives on the znodes themselves to control which nodes access them.
Sure, but ZooKeeper fills a niche. It's a tool for making a distributed applications play nice with multiple instances, not for storing/sharing large amounts of data. Compared to using an IMDG for this purpose, Zookeeper will be faster, manages heartbeats and synchronization in a predictable way (with a lot of APIs for making this part easy), and has a "push" paradigm instead of "pull" so nodes are notified very quickly of changes.
The quotation from the linked question...
A canonical example of Zookeeper usage is distributed-memory computation
... is, IMO, a bit misleading. You would use it to orchestrate the computation, not provide the data. For example, let's say you had to process rows 1-100 of a table. You might put 10 ZK nodes up, with names like "1-10", "11-20", "21-30", etc. Client applications would be notified of this change automatically by ZK, and the first one would grab "1-10" and set an ephemeral node clients/192.168.77.66/processing/rows_1_10
The next application would see this and go for the next group to process. The actual data to compute would be stored elsewhere (ie Redis, SQL database, etc). If the node failed partway through the computation, another node could see this (after 30-60 seconds) and pick up the job again.
I'd say the canonical example of ZooKeeper is leader election, though. Let's say you have 3 nodes -- one is master and the other 2 are slaves. If the master goes down, a slave node must become the new leader. This type of thing is perfect for ZK.
Consistency Guarantees
ZooKeeper is a high performance, scalable service. Both reads and write operations are designed to be fast, though reads are faster than writes. The reason for this is that in the case of reads, ZooKeeper can serve older data, which in turn is due to ZooKeeper's consistency guarantees:
Sequential Consistency
Updates from a client will be applied in the order that they were sent.
Atomicity
Updates either succeed or fail -- there are no partial results.
Single System Image
A client will see the same view of the service regardless of the server that it connects to.
Reliability
Once an update has been applied, it will persist from that time forward until a client overwrites the update. This guarantee has two corollaries:
If a client gets a successful return code, the update will have been applied. On some failures (communication errors, timeouts, etc) the client will not know if the update has applied or not. We take steps to minimize the failures, but the only guarantee is only present with successful return codes. (This is called the monotonicity condition in Paxos.)
Any updates that are seen by the client, through a read request or successful update, will never be rolled back when recovering from server failures.
Timeliness
The clients view of the system is guaranteed to be up-to-date within a certain time bound. (On the order of tens of seconds.) Either system changes will be seen by a client within this bound, or the client will detect a service outage.
I'm a newbie to Redis and I was wondering if someone could help me to understand if it can be the right tool.
This is my scenario:
I have many different nodes, everyone behaving like a master and accepting clients connections to read and write a few geographical data data and the timestamp of the incoming record.
Each master node could be hosted onto a drone that only randomly get in touch and can comunicate with others, accordind to network conditions; when this happens they should synchronize their data according to their age (only the ones more recent than a specified time).
Is there any way to achieve this by Redis or do I have to implement this feature at application level?
I tried master/slaves configuration without success and I was wondering if Redis Cluster can somewhat meet my neeeds.
I googled around, but what I found had not an answer good for me
https://serverfault.com/questions/717406/redis-multi-master-replication
Using Redis Replication on different machines (multi master)
Teo, as a matter of fact, redis don't have a multi master replication.
And the cluster shard it's data through different instances. Say you have only two redis instances. Instance1 will accept store and retrieve instance1 and instance2 data. But he will ask for, and store in, instance2 every key that does not belong to his shard.
This is not, I think, really what you want. You could give a try to PostgreSQL+BDR as PostgreSQL supports nosql store and BDR provides a real master master replication (https://wiki.postgresql.org/wiki/BDR_Project) if that's really what you need.
I work with both today (and also MongoDB). Each one with a different goal. Redis would provide a smaller overhead and memory use, fast connection and fast replication. But it won't provide multi master (if you really need it).
Good day!
Suppose we have a redis-master and several slaves. The master goal is to store all data while slaves are used for quering data for users. Hovewer quering is a bit complex and some temporary data needs to be stored. And also I want to cache the query result for a couple of minutes.
How should I configure replication to save temporary data and caches?
Redis slaves have optional support to accept writes, however you have to understand a few limitations of writable slaves before to use them, since they have non trivial issues.
Keys created on the slaves will not support expires. Actually in recent versions of Redis they appear to work but are actually leaked instead of expired, until the next time you resynchronize the slave with the master from scratch or issue FLUSHALL or alike. There are deep reasons for this issue... it is currently not clear if we'll deprecate writable slaves at all, find a solution, or deny expires for writable slaves.
You may want, anyway, to use a different Redis numerical DB (SELECT command) in order to store your intermediate data (you may use MULTI/.../MOVE/EXEC transaction in order to generate your intermediate results in the currently selected DB where data belongs, and MOVE the keys off to some other DB, so it will be clear if keys are accumulating and you can FLUSHDB from time to time).
The keys you create on your slave are volatile, they may go away in any moment when the master will resynchronize with the slave. Does not look like an issue for you since if they key is no longer there, you could recompute, but care should be take,
If you elect this slave into a master you have additional keys inside.
So there are definitely things to take in mind in this setup, however it is doable in some way. However you may want to consider alternative strategies.
Lua scripts on the slave side in order to filter your data inside Lua. Not as fast as Redis C commands often.
Precomputation of data directly in the actual data set in order to make your queries possible just using read only commands.
MIGRATE in order to migrate interesting keys from a slave to an instance (another master) designed specifically to perform post-computations.
Hard to tell what's the best strategy without in-depth analysis of the actual use case / problem, but I hope this general guidelines help.
I understand that Redis serves all data from memory, but does it persist as well across server reboot so that when the server reboots it reads into memory all the data from disk. Or is it always a blank store which is only to store data while apps are running with no persistence?
I suggest you read about this on http://redis.io/topics/persistence . Basically you lose the guaranteed persistence when you increase performance by using only in-memory storing. Imagine a scenario where you INSERT into memory, but before it gets persisted to disk lose power. There will be data loss.
Redis supports so-called "snapshots". This means that it will do a complete copy of whats in memory at some points in time (e.g. every full hour). When you lose power between two snapshots, you will lose the data from the time between the last snapshot and the crash (doesn't have to be a power outage..). Redis trades data safety versus performance, like most NoSQL-DBs do.
Most NoSQL-databases follow a concept of replication among multiple nodes to minimize this risk. Redis is considered more a speedy cache instead of a database that guarantees data consistency. Therefore its use cases typically differ from those of real databases:
You can, for example, store sessions, performance counters or whatever in it with unmatched performance and no real loss in case of a crash. But processing orders/purchase histories and so on is considered a job for traditional databases.
Redis server saves all its data to HDD from time to time, thus providing some level of persistence.
It saves data in one of the following cases:
automatically from time to time
when you manually call BGSAVE command
when redis is shutting down
But data in redis is not really persistent, because:
crash of redis process means losing all changes since last save
BGSAVE operation can only be performed if you have enough free RAM (the amount of extra RAM is equal to the size of redis DB)
N.B.: BGSAVE RAM requirement is a real problem, because redis continues to work up until there is no more RAM to run in, but it stops saving data to HDD much earlier (at approx. 50% of RAM).
For more information see Redis Persistence.
It is a matter of configuration. You can have none, partial or full persistence of your data on Redis. The best decision will be driven by the project's technical and business needs.
According to the Redis documentation about persistence you can set up your instance to save data into disk from time to time or on each query, in a nutshell. They provide two strategies/methods AOF and RDB (read the documentation to see details about then), you can use each one alone or together.
If you want a "SQL like persistence", they have said:
The general indication is that you should use both persistence methods if you want a degree of data safety comparable to what PostgreSQL can provide you.
The answer is generally yes, however a fuller answer really depends on what type of data you're trying to store. In general, the more complete short answer is:
Redis isn't the best fit for persistent storage as it's mainly performance focused
Redis is really more suitable for reliable in-memory storage/cacheing of current state data, particularly for allowing scalability by providing a central source for data used across multiple clients/servers
Having said this, by default Redis will persist data snapshots at a periodic interval (apparently this is every 1 minute, but I haven't verified this - this is described by the article below, which is a good basic intro):
http://qnimate.com/redis-permanent-storage/
TL;DR
From the official docs:
RDB persistence [the default] performs point-in-time snapshots of your dataset at specified intervals.
AOF persistence [needs to be explicitly configured] logs every write operation received by the server, that will be played again at server startup, reconstructing the
original dataset.
Redis must be explicitly configured for AOF persistence, if this is required, and this will result in a performance penalty as well as growing logs. It may suffice for relatively reliable persistence of a limited amount of data flow.
You can choose no persistence at all.Better performance but all the data lose when Redis shutting down.
Redis has two persistence mechanisms: RDB and AOF.RDB uses a scheduler global snapshooting and AOF writes update to an apappend-only log file similar to MySql.
You can use one of them or both.When Redis reboots,it constructes data from reading the RDB file or AOF file.
All the answers in this thread are talking about the possibility of redis to persist the data: https://redis.io/topics/persistence (Using AOF + after every write (change)).
It's a great link to get you started, but it is defenently not showing you the full picture.
Can/Should You Really Persist Unrecoverable Data/State On Redis?
Redis docs does not talk about:
Which redis providers support this (AOF + after every write) option:
Almost none of them - redis labs on the cloud does NOT provide this option. You may need to buy the on-premise version of redis-labs to support it. As not all companies are willing to go on-premise, then they will have a problem.
Other Redis Providers does not specify if they support this option at all. AWS Cache, Aiven,...
AOF + after every write - This option is slow. you will have to test it your self on your production hardware to see if it fits your requirements.
Redis enterpice provide this option and from this link: https://redislabs.com/blog/your-cloud-cant-do-that-0-5m-ops-acid-1msec-latency/ let's see some banchmarks:
1x x1.16xlarge instance on AWS - They could not achieve less than 2ms latency:
where latency was measured from the time the first byte of the request arrived at the cluster until the first byte of the ‘write’ response was sent back to the client
They had additional banchmarking on a much better harddisk (Dell-EMC VMAX) which results < 1ms operation latency (!!) and from 70K ops/sec (write intensive test) to 660K ops/sec (read intensive test). Pretty impresive!!!
But it defenetly required a (very) skilled devops to help you create this infrastructure and maintain it over time.
One could (falsy) argue that if you have a cluster of redis nodes (with replicas), now you have full persistency. this is false claim:
All DBs (sql,non-sql,redis,...) have the same problem - For example, running set x 1 on node1, how much time it takes for this (or any) change to be made in all the other nodes. So additional reads will receive the same output. well, it depends on alot of fuctors and configurations.
It is a nightmare to deal with inconsistency of a value of a key in multiple nodes (any DB type). You can read more about it from Redis Author (antirez): http://antirez.com/news/66. Here is a short example of the actual ngihtmare of storing a state in redis (+ a solution - WAIT command to know how much other redis nodes received the latest change change):
def save_payment(payment_id)
redis.rpush(payment_id,”in progress”) # Return false on exception
if redis.wait(3,1000) >= 3 then
redis.rpush(payment_id,”confirmed”) # Return false on exception
if redis.wait(3,1000) >= 3 then
return true
else
redis.rpush(payment_id,”cancelled”)
return false
end
else
return false
end
The above example is not suffeint and has a real problem of knowing in advance how much nodes there actually are (and alive) at every moment.
Other DBs will have the same problem as well. Maybe they have better APIs but the problem still exists.
As far as I know, alot of applications are not even aware of this problem.
All in all, picking more dbs nodes is not a one click configuration. It involves alot more.
To conclude this research, what to do depends on:
How much devs your team has (so this task won't slow you down)?
Do you have a skilled devops?
What is the distributed-system skills in your team?
Money to buy hardware?
Time to invest in the solution?
And probably more...
Many Not well-informed and relatively new users think that Redis is a cache only and NOT an ideal choice for Reliable Persistence.
The reality is that the lines between DB, Cache (and many more types) are blurred nowadays.
It's all configurable and as users/engineers we have choices to configure it as a cache, as a DB (and even as a hybrid).
Each choice comes with benefits and costs. And this is NOT an exception for Redis but all well-known Distributed systems provide options to configure different aspects (Persistence, Availability, Consistency, etc). So, if you configure Redis in default mode hoping that it will magically give you highly reliable persistence then it's team/engineer fault (and NOT that of Redis).
I have discussed these aspects in more detail on my blog here.
Also, here is a link from Redis itself.
I am using redis version 2.8.3. I want to build a redis cluster. But in this cluster there should be multiple master. This means I need multiple nodes that has write access and applying ability to all other nodes.
I could build a cluster with a master and multiple slaves. I just configured slaves redis.conf files and added that ;
slaveof myMasterIp myMasterPort
Thats all. Than I try to write something into db via master. It is replicated to all slaves and I really like it.
But when I try to write via a slave, it told me that slaves have no right to write. After that I just set read-only status of slave in redis.conf file to false. Hence, I could write something into db.
But I realize that, it is not replicated to my master replication so it is not replicated to all other slave neigther.
This means I could'not build an active-active cluster.
I tried to find something whether redis has active-active cluster capability. But I could not find exact answer about it.
Is it available to build active-active cluster with redis?
If it is, How can I do it ?
Thank you!
Redis v2.8.3 does not support multi-master setups. The real question, however, is why do you want to set one up? Put differently, what challenge/problem are you trying to solve?
It looks like the challenge you're trying to solve is how to reduce the network load (more on that below) by eliminating over-the-net reads. Since Redis isn't multi-master (yet), the only way to do it is by setting up each app server with a master and a slave (to the other master) - i.e. grand total of 4 Redis instances (and twice the RAM).
The simple scenario is when each app updates only a mutually-exclusive subset of the database's keys. In that scenario this kind of setup may actually be beneficial (at least in the short term). If, however, both apps can touch all keys or if even just one key is "shared" for writes between the apps, then you'll need to bake locking/conflict resolution/etc... logic into your apps to consolidate local master and slave differences (and that may be a bit of an overkill). In either case, however, you'll end up with too many (i.e. more than 1) Redises, which means more admin effort at the very least.
Also note that by colocating app and database on the same server you're setting yourself for near-certain scalability failure. What will happen when you need more compute resources for your apps or Redis? How will you add yet another app server to the mix?
Which brings me back to the actual problem you are trying to solve - network load. Why exactly is that an issue? Are your apps so throughput-heavy or is the network so thin that you are willing to go to such lengths? Or maybe latency is the issue that you want to resolve? Be the case as it may be, I recommended that you consider a time-proven design instead, namely separating Redis from the apps and putting it on its own resources. True, network will hit you in the face and you'll have to work around/with it (which is what everybody else does). On the other hand, you'll have more flexibility and control over your much simpler setup and that, in my book, is a huge gain.
Redis Enterprise has had this feature for quite a while, but if you are looking for an open source solution KeyDB is a fork with Active Active support (called Active Replica).
Setting it up is just a little more work than standard replication:
Both servers must have "active-replica yes" in their respective configuration files
On server B execute the command "replicaof [A address] [A port]"
Server B will drop its database and load server A's dataset
On server A execute the command "replicaof [B address] [B port]"
Server A will drop its database and load server B's dataset (including the data it just transferred in the prior step)
Both servers will now propagate writes to each other. You can test this by writing to a key on Server A and ensuring it is visible on B and vice versa.
https://github.com/JohnSully/KeyDB/wiki/KeyDB-(Redis-Fork):-Active-Replica-Support