I have a MongoDB 4.4 server (single node, no replicas) for storing IoT-style data. Data is written to several collections every few seconds by my NodeJS app. Documents are not updated/modified, and reads are less common than writes.
I have TTL indices on my big collections so that data older than 6 months is deleted. However, Mongo seems to consume more and more disk space. When the disk inevitably fills up, Mongo and my app stop working. I need to stop Mongo from consuming increasing amounts of disk space.
If I call stats() on my big collections, I can see that there are gigabytes of "file bytes available for reuse". But when I use db.runCommand({compact:'big_collection'}), it doesn't seem to release any space. Other people seem to have similar experiences. I wish I understood why compact isn't working.
I suspect the best alternative approach is to remove the TTL index, and then Cap the collection to a fixed size, but I'd like to hear if anyone has experience with such a process, or alternative recommendations.
Use case
I am using MongoDB to persist messages from a message queue system (e. g. RabbitMQ / Kafka). Each message has a timestamp and based on that timestamp I want to expire the documents 1 hour afterwards. Therefore I've got a deleteAt field which is indexed and has set expireAfterSeconds: 0. Everything works fine, except if MongoDB is under heavy load.
We are inserting roughly 5-7k messages / second into a single replica set. The TTL Thread seems to be way slower than the rate of message coming in and thus the storage is quickly growing (which we want to avoid with TTLs).
To describe the behaviour more exactly, when I sort the messages by deleteAt ascending (oldest date first) I can see that it sometimes does not delete any of those messages for hours. Because of this observation I believe that the TTL thread sometimes is stuck or not active at all.
My question
What could I do to ensure that the TTL thread is not negatively impacted by the rate of messages coming in? According to our metrics our only bottleneck seems to be CPU, even though the SSD disk I/O is pretty high too.
Do I need to tune something (e. g. give MongoDB more threads for document deletion) so that the TTL thread can keep up with the write rate?
I believe I am facing a known bug as described in MongoDB's Jira Dashboard: https://jira.mongodb.org/browse/SERVER-19334
From https://docs.mongodb.com/manual/core/index-ttl/:
The background task that removes expired documents runs every 60 seconds. As a result, documents may remain in a collection during the period between the expiration of the document and the running of the background task.
Because the duration of the removal operation depends on the workload of your mongod instance, expired data may exist for some time beyond the 60 second period between runs of the background task.
I'm not aware of any way to tune that TTL thread, and I suspect you'll need to run your own cron to do batched deletes.
The other thing to look at might be what's taking up CPU and IO and see if there's any way of reducing that load.
You can create the index with "sparse", this should perform the clean up on a separate thread in the background.
our software design assumes a database per user in an attempt to partition the data and later be able to distribute and load balance per user.
We noticed that the mongod process is taking a lot of memory even when no user has ever logged in.
So I would like to know how/when the loading occurs, if there is a setting that could do some lazy loading or if there is a better strategy to achieve what we want.
Thank you
I believe got the answer digging into MongoDB documentation. Databases are always "loaded" regardless if active or idle.
To calculate how much RAM you need, you must calculate your working set size, or the portion of your data that clients use most often. This depends on your access patterns, what indexes you have, and the size of your documents. Because MongoDB uses a thread per connection model, each database connection also will need up to 1 MB of RAM, whether active or idle.
So for a very high number of databases even when only a few are active, memory could be a big concern and our strategy seems need to be changed.
I've been read a lot about MongoDB recently, but one topic I can't find any clear material on, is how data is written to the journal and oplog.
So this is what I understand of the process so far, please correct me where I'm wrong
A client connect to mongod and performs a write. The write is stored in the socket buffer
When Mongo is available (not sure what available means at this point), data is written to the journal?
The mongoDB docs then say that writes every 60 seconds are flushed from the journal onto disk. By this I can only assume this mean written to the primary and the oplog. If this is the case, how to writes appear earlier than the 60 seconds sync interval?
Some time later, secondaries suck data from the primary or their sync source and update their oplog and databases. It seems very vague about when exactly this happens and what delays it.
I'm also wondering if journaling was disabled (I understand that's a really bad idea), at what point does the oplog and database get updated?
Lastly I'm a bit stumpted at which points in this process, the write locks get created. Is this just when the database and oplog are updated or at other times too?
Thanks to anyone who can shed some light on this or point me to some reading material.
Simon
Here is what happens as far as I understand it. I simplified a bit, but it should make clear how it works.
A client connects to mongo. No writes done so far, and no connection torn down, because it really depends on the write concern what happens now.Let's assume that we go with the (by the time of this writing) default "acknowledged".
The client sends it's write operation. Here is where I am really not sure. Either after this step or the next one the acknowledgement is sent to the driver.
The write operation is run through the query optimizer. It is here where the acknowledgment is sent because with in an acknowledged write concern, you may be returned a duplicate key error. It is possible that this was checked in the last step. If I should bet, I'd say it is after this one.
The output of the query optimizer is then applied to the data in memory Actually to the data of the memory mapped datafiles, to the memory mapped oplog and to the journal's memory mapped files. Queries are answered from this memory mapped parts or the according data is mapped to memory for answering the query. The oplog is read from memory if present, too.
Every 100ms in general the journal is synced to disk. The precise value is determined by a number of factors, one of them being the journalCommitInterval configuration parameter. If you have a write concern of journaled, the driver will be notified now.
Every syncDelay seconds, the current state of the memory mapped files is synced to disk I think the journal is truncated to the entries which weren't applied to the data yet, but I am not too sure of that since that it should basically never happen that data in the journal isn't yet applied to the current data.
If you have read carefully, you noticed that the data is ready for the oplog as early as it has been run through the query optimizer and was applied to the files mapped into memory. When the oplog entry is pulled by one of the secondaries, it is immediately applied to it's data of the memory mapped files and synced in the disk the same way as on the primary.
Some things to note: As soon as the relatively small data is written to the journal, it is quite safe. If a node goes down between two syncs to the datafiles, both the datafiles and the oplog can be restored from their last state in the datafiles and the journal. In general, the maximum data loss you can have is the operations recorded into the log after the last commit, 50ms in median.
As for the locks. If you have written carefully, there aren't locks imposed on a database level when the data is synced to disk. Write locks may be created in order to assure that only one thread at any given point in time modifies a given document. There are other write locks possible , but in general, they should be rather rare.
Write locks on the filesystem layer are created once, though only implicitly, iirc. During application startup, a lock file is created in the root directory of the dbpath. Any other mongod instance will refuse to do any operation on those datafiles while a valid lock exists. And you shouldn't either ;)
Hope this helps.
Even if journaling is on, is there still a chance to lose writes in MongoDB?
"By default, the greatest extent of lost writes, i.e., those not made to the journal, are those made in the last 100 milliseconds."
This is from Manage Journaling, which indicates you could lose writes made since the last time the journal was flushed to disk.
If I want more durability, "To force mongod to commit to the journal more frequently, you can specify j:true. When a write operation with j:true is pending, mongod will reduce journalCommitInterval to a third of the set value."
Even in this case, it looks like flushing the journal to disk is asynchronous so there is still a chance to lose writes. Am I missing something about how to guarantee that writes are not lost?
Posting a new answer to clean this up. I performed tests and read the source code again and I'm sure the irritation comes from an unfortunate sentence in the write concern documentation. With journaling enabled and j:true write concern, the write is durable, and there is no mysterious window for data loss.
Even if journaling is on, is there still a chance to lose writes in MongoDB?
Yes, because the durability also depends on the individual operations write concern.
"By default, the greatest extent of lost writes, i.e., those not made to the journal, are those made in the last 100 milliseconds."
This is from Manage Journaling, which indicates you could lose writes made since the last time the journal was flushed to disk.
That is correct. The journal is flushed by a separate thread asynchronously, so you can lose everything since the last flush.
If I want more durability, "To force mongod to commit to the journal more frequently, you can specify j:true. When a write operation with j:true is pending, mongod will reduce journalCommitInterval to a third of the set value."
This irritated me, too. Here's what it means:
When you send a write operation with j:true, it doesn't trigger the disk flush immediately, and not on the network thread. That makes sense, because there could be dozens of applications talking to the same mongod instance. If every application were to use journaling a lot, the db would be very slow because it's fsyncing all the time.
Instead, what happens is that the 'durability thread' will take all pending journal commits and flush them to disk. The thread is implemented like this (comments mine):
sleepmillis(oneThird); //dur.cpp, line 801
for( unsigned i = 1; i <= 2; i++ ) {
// break, if any j:true write is pending
if( commitJob._notify.nWaiting() )
break;
// or the number of bytes is greater than some threshold
if( commitJob.bytes() > UncommittedBytesLimit / 2 )
break;
// otherwise, sleep another third
sleepmillis(oneThird);
}
// fsync all pending writes
durThreadGroupCommit();
So a pending j:true operation will cause the journal commit thread to commit earlier than it normally would, and it will commit all pending writes to the journal, including those that don't have j:true set.
Even in this case, it looks like flushing the journal to disk is asynchronous so there is still a chance to lose writes. Am I missing something about how to guarantee that writes are not lost?
The write (or the getLastError command) with a j:true journaled write concern will wait for the durability thread to finish syncing, so there's no risk of data loss (as far as the OS and hardware guarantee that).
The sentence "However, there is a window between journal commits when the write operation is not fully durable" probably refers to a mongod running with journaling enabled that accepts a write that does NOT use the j:true write concern. In that case, there's a chance of the write getting lost since the last journal commit.
I filed a docs bug report for this.
Maybe. Yes, it waits for the data to be written, but according to the docs there's a 'there is a window between journal commits when the write operation is not fully durable', whatever that is. I couldn't find out what they refer to.
I'm leaving the edited answer here, but I reversed myself back-and-forth, so it's a bit irritating:
This is a bit tricky, because there are a lot of levers you can pull:
Your MongoDB setup
Assuming that journaling is activated (default for 64 bit), the journal will be committed in regular intervals. The default value for the journalCommitInterval is 100ms if the journal and the data files are on the same block device, or 30ms if they aren't (so it's preferable to have the journal on a separate disk).
You can also change the journalCommitInterval to as little as 2ms, but it will increase the number of write operations and reduce overall write performance.
The Write Concern
You need to specify a write concern that tells the driver and the database to wait until the data is written to disk. However, this won't wait until the data has been actually written to the disk, because that would take 100ms in a bad-case scenario with the default setup.
So, at the very best, there's a 2ms window where data can get lost. That's insufficient for a number of applications, however.
The fsync command forces a disk flush of all data files, but that's unnecessary if you use journaling, and it's inefficient.
Real-Life Durability
Even if you were to journal every write, what is it good for if the datacenter administrator has a bad day and uses a chainsaw on your hardware, or the hardware simply disintegrates itself?
Redundant storage, not on a block device level like RAID, but on a much higher level is a better option for many scenarios: Have the data in different locations or at least on different machines using a replica set and use the w:majority write concern with journaling enabled (journaling will only apply on the primary, though). Use RAID on the individual machines to increase your luck.
This offers the best tradeoff of performance, durability and consistency. Also, it allows you to adjust the write concern for every write and has good availability. If the data is queued for the next fsync on three different machines, it might still be 30ms to the next journal commit on any of the machines (worst case), but the chance of three machines going down within the 30ms interval is probably a millionfold lower than the chainsaw-massacre-admin scenario.
Evidence
TL;DR: I think my answer above is correct.
The documentation can be a little irritating, especially with regards to wtimeout, so I checked the source. I'm not an expert on the mongo source, so take this with a grain of salt:
In write_concern.cpp, we find (edited for brevity):
if ( cmdObj["j"].trueValue() ) {
if( !getDur().awaitCommit() ) {
// --journal is off
result->append("jnote", "journaling not enabled on this server");
} // ...
}
else if ( cmdObj["fsync"].trueValue() ) {
if( !getDur().awaitCommit() ) {
// if get here, not running with --journal
log() << "fsync from getlasterror" << endl;
result->append( "fsyncFiles" , MemoryMappedFile::flushAll( true ) );
}
Note the call MemoryMappedFile::flushAll( true ) if fsync is set. This call is clearly not in the first branch. Otherwise, durability is handled on a sepate thread (relevant files prefixed dur_).
That explains what wtimeout is for: it refers to the time waiting for slaves, and has nothing to do with I/O or fsync on the server.
Journaling is for keeping the data on a particular mongod in a consistent state, even in case of chainsaw madness, however with client settings through writeconcern it can be used to force out durability. About write concern DOCS.
There is an option, j:1, which you can read about here which ensures that the particular write operation waits for acknowledge till it is written to the journal file on disk (so not just in the memory map). However this docs says the opposite. :) I would vote for the first case it makes me feel more comfortable.
If you run lots of commands with such option mongodb will adapt the size of the commit interval of the journal to speed things up, you can read about it here: DOCS this one you also mentioned and as others already said that you can specify an interval between 2-300ms.
Durability is much more ensured in my opinion over the w:2 option while if the update/write operation is acknowledged by two members in a replicaset it is really unlikely to lose both in the same minute (datafile flush interval), but not impossible.
Using both options will cause the situation that when the operation is acknowledged by the database cluster it will reside in memory at two different boxes and on one it will be in a consistent recoverable disk place too.
Generally lost writes are an issue in every system where there is buffering/caching/delayed-write involved between a system's runtime and a permanent (non-volatile) storage, even at the OS level (for example write-behind caching). So there is always a chance to lose writes, even if your concrete provider (MongoDB) provides functionality for transaction durability it's the underlying OS that is responsible for ultimately writing the data, and even then there is caching at the device level... And that's just the lower levels, making the system highly concurrent, distributed and performant only makes matters worse.
In short there is no absolute durability, only practical/eventual/hope-for-the-best durability especially with a NoSQL storage like Mongo, which isn't primarily made for consistency and durability in the first place.
I would have to agree with Sammaye that journoualing has little to do with durability. However, if you want to get an answer to whether you can really trust mongodb to store your data with good consistency, then I would suggest that you read this blog post. There is a reply from 10gen regarding that post, and a reply from the author to the 10gen post. I would suggest that you read into it to make an educated decision. It took me some time to understand all the details on my own, but this post has the basics covered.
The response to the blog post was given here by 10gen, the company that makes mongodb.
And the response to the response was given by the professor on this post.
It explains a lot about how Mongodb can shard data, how it actually functions, and the performance hits it takes if you add on extra safety locks. I strongly want to say that these three writings are the best thing out there, and by far the most comprehensive things out there that talk about the benefits and drawbacks of mongodb, if you think its one sided, look at the comments, and also see what people had to say, because if something received a reply from the company that made the software, then it must have made some good points atleast.