I use both Cassandra and ScyllaDB 3-node clusters and use PySpark to read data. I was wondering if any of them are not repaired forever, is there any challenge while reading data from either if there are inconsistencies in nodes. Will the correct data be read and if yes, then why do we need to repair them?
Yes you can get incorrect data if reapir is not done. It also depends on with what consistency you are reading or writing. Generally in production systems writes are done with (Local_one/Local_quorum) and read with Local_quorum.
If you are writing with weak consistency level, then repair becomes important as some of the nodes might not have got the mutations and while reading those nodes may get selected.
For example if you write with consistency level ONE on a table TABLE1 with a replication of 3. Now it may happen your write was written to NodeA only and NodeB and NodeC might have missed the mutation. Now if you are reading with Consistency level LOCAL_QUORUM, it may happen that NodeB and 'NodeC' get selected and they do not return the written data.
Repair is an important maintenance task for Cassandra which should be done periodically and continuously to keep data in healthy state.
As others have noted in other answers, different consistency levels make repair more or less important for different reasons. So I'll focus on the consistency level that you said in a comment you are using: LOCAL_ONE for reading and LOCAL_QUORUM for writing:
Successfully writing with LOCAL_QUORUM only guarantees that two replicas have been written. If the third replica is temporarily down, and will later come up - at that point one third of the read requests for this data, reads done from only one node (this is what LOCAL_ONE means) will miss the new data! Moreover, there isn't even a guarantee of so-called monotonic consistency - you can get new data in one read (from one node), and the old data in a later read (from another node).
However, it isn't completely accurate that only a repair can fix this problem. Another feature - enabled by default on both Cassandra and Scylla - is called Hinted Handoff - where when a node is down for relatively short time (up to three hours, but also depending on the amount of traffic in that period), other nodes which tried to send it updates remember those updates - and retry the send when the dead node comes back up. If you are faced only with such relatively short downtimes, repair isn't necessary and Hinted Handoff is actually enough.
That being said, Hinted Handoff isn't guaranteed perfect and might miss some inconsistencies. E.g., the node wishing to save a hint might itself be rebooted before it managed to save the hint, or replaced after saving it. So this mechanism isn't completely foolproof.
By the way, there another thing you need to be aware of: If you ever intend to do a repair (e.g., perhaps after some node was down for too long for Hinted Handoff to have worked, or perhaps because a QUORUM read causes a read repair), you must do it at least once every gc_grace_seconds (this defaults to 10 days).
The reason for this statement is the risk of data resurrection by repair which is too infrequent. The thing is, after gc_grace_seconds, the tombstones marking deleted items are removed forever ("garbage collected"). At that point, if you do a repair and one of the nodes happens to have an old version of this data (prior to the delete), the old data will be "resurrected" - copied to all replicas.
In addition to Manish's great answer, I'll just add that read operations run consistency levels higher than *_ONE have a (small...10% default) chance to invoke a read repair. I have seen that applications running at a higher consistency level for reads, will have less issues with inconsistent replicas.
Although, writing at *_QUORUM should ensure that the majority (quorum) of replicas are indeed consistent. Once it's written successfully, data should not "go bad" over time.
That all being said, running periodic (weekly) repairs is a good idea. I highly recommend using Cassandra Reaper to manage repairs, especially if you have multiple clusters.
Related
In a distributed system, if only half of the nodes are successfully written, the subsequent nodes that read the unwritten data will be inconsistent. How to avoid this situation?
client write --> Node1 v
--> Node2 v
client read --> Node3 x(The latest data was not read)
My plan:
Compare the data version with other nodes when reading data
If the current node version is found to be lower, it will be routed to other nodes to read data.
I am going to ignore tags [mongo and elastic] :)
What you are planning to do is called Dynamo style replication. That system is eventually consistent by design. (I read a while ago that it could get strongly consistent with some effort, but I don't remember if that paper was correct.)
Back to dynamo and quorum: with three nodes you want to have at least 2 nodes to save writes to assume the write has succeeded. Important point is that you need two nodes to report back to customer the success, but the data is still should to be sent to three nodes.
Let's assume that data is written to two nodes, third failed, but camed back online later. To read the data, you have to read it from any two nodes as well. You will sent read requests to all three, but only two is needed to report back to the customer. This will give you quorum: 2+2>3. This guarantees that there is an intersection in between writes and reads.
This will work ok when the network is good and nodes are healthy. But you will run into major challengers, lost updates and conflict resolution to name a few. But in either way, the system will not be strongly consistent based on design itself.
Let me describe another interesting issue to illustrate weak consistency:
node 1 gets the write
the rest of process fails; node 1 has new data, but node 2 and 3 don't
now, when you read, under the quorum condition, you may or may not see the value from node 1 - since you are picking any two nodes for a read, node 1 may not be in that set.
Long story short, dynamo is not good for strong consistency, and we get to the Raft part of the solution.
Raft will get you what you need. A consistent system. There is a catch to watch for. Most examples are focused on writing - raft maintains a log of messages and consensus is used to agree on the order (and content) of these messages.
But when you do a read, you can't just go to a node, or any two nodes, or three and read the value. You will have to do read via Raft as well, by attaching a read operation to raft's log. This is called linearizable read.
I'll stop here, as this is pretty complicated topic (but not an impossible one to learn).
Hope this gave you enough ideas to explore.
I saw both mongodb and elasticsearch is being tagged, I don't know which case you are thinking, but the two database is very different.
For mongo, replicas are not by default used to increase reading speed, see https://docs.mongodb.com/manual/core/read-preference, the default reading preferences will only look at primary and excludes all replicas. The writing of Mongo is also to the primary first and the replication will happen asynchronously possibly after the write to primary finishes, see https://docs.mongodb.com/manual/core/replica-set-members/. Because of that, if you do a force read to the secondary, you are not guaranteed to have the newest data.
For elasticsearch, elasticsearch naturally does not guarantee you always read the most recent data, see https://www.elastic.co/guide/en/elasticsearch/reference/current/near-real-time.html, so in either way even if there is only one node you may get data that are out of date.
My understanding of the CAP acronym is as follows:
Consistent: every read gets the most recent write
Available: every node is available
Partion Tolerant: the system can continue upholding A and C promises when the network connection between nodes goes down
Assuming my understanding is more or less on track, then something is bother me.
AFAIK, availability is achieved via any of the following techniques:
Load balancing
Replication to a disaster recovery system
So if I have a system that I already know is CP, why can't I "make it full CAP" by applying one of these techniques to make it available as well? I'm sure I'm missing something important here, just not sure what.
It's the partition tolerance, that you got wrong.
As long as there isn't any partitioning happening, systems can be consistent and available. There are CA systems which say, we don't care about partitions. You can have them running inside racks with server hardware and make partitioning extremely unlikely. The problem is, what if partitions occur?
The system can either choose to
continue providing the service, hoping the other server is down rather than providing the same service and serving different data - choosing availability (AP)
stop providing the service, because it couldn't guarantee consistency anymore, since it doesn't know if the other server is down or in fact up and running and just the communication between these two broke off - choosing consistency (CP)
The idea of the CAP theorem is that you cannot provide both Availability AND Consistency, once partitioning occurs, you can either go for availability and hope for the best, or play it safe and be unavailable, but consistent.
Here are 2 great posts, which should make it clear:
You Can’t Sacrifice Partition Tolerance shows the idea, that every truly distributed system needs to deal with partitioning now and than and hence CA systems will break instantly at the first occurrence of a partition
CAP Twelve Years Later: How the "Rules" Have Changed is slightly more up to date and shows the CAP theorem more flexible, where developers can choose how applications behave during partitioning and can sacrifice a bit of consistency to gain some availability, ...
So to finally answer your question, if you take a CP system and replicate it more often, you might either run into overhead of messages sent between the nodes of the system to keep it consistent, or - in case a substantial part of the nodes fails or network partitioning occurs without any part having a clear majority, it won't be able to continue operation as it wouldn't be able to guarantee consistency anymore. But yes, these lines are getting more blurred now and I think the references I've provided will give you a much better understanding.
Everywhere I look, I see that MongoDB is CP.
But when I dig in I see it is eventually consistent.
Is it CP when you use safe=true? If so, does that mean that when I write with safe=true, all replicas will be updated before getting the result?
MongoDB is strongly consistent by default - if you do a write and then do a read, assuming the write was successful you will always be able to read the result of the write you just read. This is because MongoDB is a single-master system and all reads go to the primary by default. If you optionally enable reading from the secondaries then MongoDB becomes eventually consistent where it's possible to read out-of-date results.
MongoDB also gets high-availability through automatic failover in replica sets: http://www.mongodb.org/display/DOCS/Replica+Sets
I agree with Luccas post. You can't just say that MongoDB is CP/AP/CA, because it actually is a trade-off between C, A and P, depending on both database/driver configuration and type of disaster: here's a visual recap, and below a more detailed explanation.
Scenario
Main Focus
Description
No partition
CA
The system is available and provides strong consistency
partition, majority connected
AP
Not synchronized writes from the old primary are ignored
partition, majority not connected
CP
only read access is provided to avoid separated and inconsistent systems
Consistency:
MongoDB is strongly consistent when you use a single connection or the correct Write/Read Concern Level (Which will cost you execution speed). As soon as you don't meet those conditions (especially when you are reading from a secondary-replica) MongoDB becomes Eventually Consistent.
Availability:
MongoDB gets high availability through Replica-Sets. As soon as the primary goes down or gets unavailable else, then the secondaries will determine a new primary to become available again. There is an disadvantage to this: Every write that was performed by the old primary, but not synchronized to the secondaries will be rolled back and saved to a rollback-file, as soon as it reconnects to the set(the old primary is a secondary now). So in this case some consistency is sacrificed for the sake of availability.
Partition Tolerance:
Through the use of said Replica-Sets MongoDB also achieves the partition tolerance: As long as more than half of the servers of a Replica-Set is connected to each other, a new primary can be chosen. Why? To ensure two separated networks can not both choose a new primary. When not enough secondaries are connected to each other you can still read from them (but consistency is not ensured), but not write. The set is practically unavailable for the sake of consistency.
As a brilliant new article showed up and also some awesome experiments by Kyle in this field, you should be careful when labeling MongoDB, and other databases, as C or A.
Of course CAP helps to track down without much words what the database prevails about it, but people often forget that C in CAP means atomic consistency (linearizability), for example. And this caused me lots of pain to understand when trying to classify. So, besides MongoDB give strong consistency, that doesn't mean that is C. In this way, if one make this classifications, I recommend to also give more depth in how it actually works to not leave doubts.
Yes, it is CP when using safe=true. This simply means, the data made it to the masters disk.
If you want to make sure it also arrived on some replica, look into the 'w=N' parameter where N is the number of replicas the data has to be saved on.
see this and this for more information.
MongoDB selects Consistency over Availability whenever there is a Partition. What it means is that when there's a partition(P) it chooses Consistency(C) over Availability(A).
To understand this, Let's understand how MongoDB does replica set works. A Replica Set has a single Primary node. The only "safe" way to commit data is to write to that node and then wait for that data to commit to a majority of nodes in the set. (you will see that flag for w=majority when sending writes)
Partition can occur in two scenarios as follows :
When Primary node goes down: system becomes unavailable until a new
primary is selected.
When Primary node looses connection from too many
Secondary nodes: system becomes unavailable. Other secondaries will try to
elect a new Primary and current primary will step down.
Basically, whenever a partition happens and MongoDB needs to decide what to do, it will choose Consistency over Availability. It will stop accepting writes to the system until it believes that it can safely complete those writes.
Mongodb never allows write to secondary. It allows optional reads from secondary but not writes. So if your primary goes down, you can't write till a secondary becomes primary again. That is how, you sacrifice High Availability in CAP theorem. By keeping your reads only from primary you can have strong consistency.
I'm not sure about P for Mongo. Imagine situation:
Your replica gets split into two partitions.
Writes continue to both sides as new masters were elected
Partition is resolved - all servers are now connected again
What happens is that new master is elected - the one that has highest oplog, but the data from the other master gets reverted to the common state before partition and it is dumped to a file for manual recovery
all secondaries catch up with the new master
The problem here is that the dump file size is limited and if you had a partition for a long time you can loose your data forever.
You can say that it's unlikely to happen - yes, unless in the cloud where it is more common than one may think.
This example is why I would be very careful before assigning any letter to any database. There's so many scenarios and implementations are not perfect.
If anyone knows if this scenario has been addressed in later releases of Mongo please comment! (I haven't been following everything that was happening for some time..)
Mongodb gives up availability. When we talk about availability in the context of the CAP theorem, it is about avoiding single points of failure that can go down. In mongodb. there is a primary router host. and if that goes down,there is gonna be some downtime in the time that it takes for it to elect a new replacement server to take its place. In practical, that is gonna happen very qucikly. we do have a couple of hot standbys sitting there ready to go. So as soon as the system detects that primary routing host went down, it is gonna switch over to a new one pretty much right away. Technically speaking it is still single point of failure. There is still a chance of downtime when that happens.
There is a config server, that is the primary and we have an app server, that is primary at any given time. even though we have multiple backups, there is gonna be a brief period of downtime if any of those servers go down. the system has to first detect that there was an outage and then remaining servers need to reelect a new primary host to take its place. that might take a few seconds and this is enough to say that mongodb is trading off the availability
Everywhere I look, I see that MongoDB is CP.
But when I dig in I see it is eventually consistent.
Is it CP when you use safe=true? If so, does that mean that when I write with safe=true, all replicas will be updated before getting the result?
MongoDB is strongly consistent by default - if you do a write and then do a read, assuming the write was successful you will always be able to read the result of the write you just read. This is because MongoDB is a single-master system and all reads go to the primary by default. If you optionally enable reading from the secondaries then MongoDB becomes eventually consistent where it's possible to read out-of-date results.
MongoDB also gets high-availability through automatic failover in replica sets: http://www.mongodb.org/display/DOCS/Replica+Sets
I agree with Luccas post. You can't just say that MongoDB is CP/AP/CA, because it actually is a trade-off between C, A and P, depending on both database/driver configuration and type of disaster: here's a visual recap, and below a more detailed explanation.
Scenario
Main Focus
Description
No partition
CA
The system is available and provides strong consistency
partition, majority connected
AP
Not synchronized writes from the old primary are ignored
partition, majority not connected
CP
only read access is provided to avoid separated and inconsistent systems
Consistency:
MongoDB is strongly consistent when you use a single connection or the correct Write/Read Concern Level (Which will cost you execution speed). As soon as you don't meet those conditions (especially when you are reading from a secondary-replica) MongoDB becomes Eventually Consistent.
Availability:
MongoDB gets high availability through Replica-Sets. As soon as the primary goes down or gets unavailable else, then the secondaries will determine a new primary to become available again. There is an disadvantage to this: Every write that was performed by the old primary, but not synchronized to the secondaries will be rolled back and saved to a rollback-file, as soon as it reconnects to the set(the old primary is a secondary now). So in this case some consistency is sacrificed for the sake of availability.
Partition Tolerance:
Through the use of said Replica-Sets MongoDB also achieves the partition tolerance: As long as more than half of the servers of a Replica-Set is connected to each other, a new primary can be chosen. Why? To ensure two separated networks can not both choose a new primary. When not enough secondaries are connected to each other you can still read from them (but consistency is not ensured), but not write. The set is practically unavailable for the sake of consistency.
As a brilliant new article showed up and also some awesome experiments by Kyle in this field, you should be careful when labeling MongoDB, and other databases, as C or A.
Of course CAP helps to track down without much words what the database prevails about it, but people often forget that C in CAP means atomic consistency (linearizability), for example. And this caused me lots of pain to understand when trying to classify. So, besides MongoDB give strong consistency, that doesn't mean that is C. In this way, if one make this classifications, I recommend to also give more depth in how it actually works to not leave doubts.
Yes, it is CP when using safe=true. This simply means, the data made it to the masters disk.
If you want to make sure it also arrived on some replica, look into the 'w=N' parameter where N is the number of replicas the data has to be saved on.
see this and this for more information.
MongoDB selects Consistency over Availability whenever there is a Partition. What it means is that when there's a partition(P) it chooses Consistency(C) over Availability(A).
To understand this, Let's understand how MongoDB does replica set works. A Replica Set has a single Primary node. The only "safe" way to commit data is to write to that node and then wait for that data to commit to a majority of nodes in the set. (you will see that flag for w=majority when sending writes)
Partition can occur in two scenarios as follows :
When Primary node goes down: system becomes unavailable until a new
primary is selected.
When Primary node looses connection from too many
Secondary nodes: system becomes unavailable. Other secondaries will try to
elect a new Primary and current primary will step down.
Basically, whenever a partition happens and MongoDB needs to decide what to do, it will choose Consistency over Availability. It will stop accepting writes to the system until it believes that it can safely complete those writes.
Mongodb never allows write to secondary. It allows optional reads from secondary but not writes. So if your primary goes down, you can't write till a secondary becomes primary again. That is how, you sacrifice High Availability in CAP theorem. By keeping your reads only from primary you can have strong consistency.
I'm not sure about P for Mongo. Imagine situation:
Your replica gets split into two partitions.
Writes continue to both sides as new masters were elected
Partition is resolved - all servers are now connected again
What happens is that new master is elected - the one that has highest oplog, but the data from the other master gets reverted to the common state before partition and it is dumped to a file for manual recovery
all secondaries catch up with the new master
The problem here is that the dump file size is limited and if you had a partition for a long time you can loose your data forever.
You can say that it's unlikely to happen - yes, unless in the cloud where it is more common than one may think.
This example is why I would be very careful before assigning any letter to any database. There's so many scenarios and implementations are not perfect.
If anyone knows if this scenario has been addressed in later releases of Mongo please comment! (I haven't been following everything that was happening for some time..)
Mongodb gives up availability. When we talk about availability in the context of the CAP theorem, it is about avoiding single points of failure that can go down. In mongodb. there is a primary router host. and if that goes down,there is gonna be some downtime in the time that it takes for it to elect a new replacement server to take its place. In practical, that is gonna happen very qucikly. we do have a couple of hot standbys sitting there ready to go. So as soon as the system detects that primary routing host went down, it is gonna switch over to a new one pretty much right away. Technically speaking it is still single point of failure. There is still a chance of downtime when that happens.
There is a config server, that is the primary and we have an app server, that is primary at any given time. even though we have multiple backups, there is gonna be a brief period of downtime if any of those servers go down. the system has to first detect that there was an outage and then remaining servers need to reelect a new primary host to take its place. that might take a few seconds and this is enough to say that mongodb is trading off the availability
I am working on a project which has some important data in it. This means we cannot to lose any of it if the light or server goes down. We are using MongoDB for the database. I'd like to be sure that my data is in the database after the insert and rollback the whole batch if one element was not inserted. I know it is the philosophy behind Mongo that we do not need transactions but how can I make sure that my data is really safely stored after insert rather than sent to some "black hole".
Should I make a search?
Should I use some specific mongoDB commands?
Should I use sharding even if one server is enough for satisfying
the speed and by the way it doesn't guarantee anything if the light
goes down?
What is the best solution?
Your best bet is to use Write Concerns - these allow you to tell MongoDB how important a piece of data is. The quickest Write Concern is also the least safe - the data is not flushed to disk until the next scheduled flush. The safest will confirm that the data has been written to disk on a number of machines before returning.
The write concern you are looking for is FSYNC_SAFE (at least that is what it is called from the point of view of the Java driver) or REPLICAS_SAFE which confirms that your data has been replicated.
Bear in mind that MongoDB does not have transactions in the traditional sense - your rollback will have to be rolled by hand as you can't tell the Mongo database to do this for you.
The other thing you need to do is either use the relatively new --journal option (which uses a Write Ahead Log), or use replica sets to share your data across many machines in order to maximise data integrity in the event of a crash/power loss.
Sharding is not so much a protection against hardware failure as a method for sharing the load when dealing with particularly large datasets - sharding shouldn't be confused with replica sets which is a way of writing data to more than one disk on more than one machine.
Therefore, if your data is valuable enough, you should definitely be using replica sets, perhaps even siting slaves in other data centres/availability zones/racks/etc in order to provide the resilience you require.
There is/will be (can't remember offhand whether this has been implemented yet) a way to specify the priority of individual nodes in a replica set such that if the master goes down the new master that is elected is one in the same data centre if such a machine is available (ie to stop a slave on the other side of the country from becoming master unless it really is the only other option).
I received a really nice answer from a person called GVP on google groups. I will quote it(basically it adds up to Rich's answer):
I'd like to be sure that my data is in the database after the
insert and rollback the whole batch if one element was not inserted.
This is a complex topic and there are several trade-offs you have to
consider here.
Should I use sharding?
Sharding is for scaling writes. For data safety, you want to look a
replica sets.
Should I use some specific mongoDB commands?
First thing to consider is "safe" mode or "getLastError()" as
indicated by Andreas. If you issue a "safe" write, you know that the
database has received the insert and applied the write. However,
MongoDB only flushes to disk every 60 seconds, so the server can fail
without the data on disk.
Second thing to consider is "journaling"
(v1.8+). With journaling turned on, data is flushed to the journal
every 100ms. So you have a smaller window of time before failure. The
drivers have an "fsync" option (check that name) that goes one step
further than "safe", it waits for acknowledgement that the data has
be flushed to the disk (i.e. the journal file). However, this only
covers one server. What happens if the hard drive on the server just
dies? Well you need a second copy.
Third thing to consider is
replication. The drivers support a "W" parameter that says "replicate
this data to N nodes" before returning. If the write does not reach
"N" nodes before a certain timeout, then the write fails (exception
is thrown). However, you have to configure "W" correctly based on the
number of nodes in your replica set. Again, because a hard drive
could fail, even with journaling, you'll want to look at replication.
Then there's replication across data centers which is too long to get
into here. The last thing to consider is your requirement to "roll
back". From my understanding, MongoDB does not have this "roll back"
capacity. If you're doing a batch insert the best you'll get is an
indication of which elements failed.
Here's a link to the PHP driver on this one: http://it.php.net/manual/en/mongocollection.batchinsert.php You'll have to check the details on replication and the W parameter. I believe the same limitations apply here.