We are using MongoDb(3.0.6) for the application we are using. We have around 150 entries in one of the collections, but it takes approx 500ms to fetch all of these records, which I didn't expect. Mongo is deployed on a company server. What can be done to reduce this time?
We are not getting too many reads and there is no CPU load, etc, What can be mistake which may be causing this or what config should be changes to affect these.
Here is my schema: http://pastebin.com/x18iDPKf
I am just querying all the entries, which are 160 in number. I don't think time taken is due to Mongoose or NodeJs, as when I quesry using RoboMongo, It still takes same time.
Output of db.<collection>.stats().size :
223000
The query I am doing is:
db.getCollection('collectionName').find({})
Definitely it shouldn't be a problem with MongoDB. It should be a temporary issue or might be due to system constraints such as Internal Memory and so on.
If still the problem exists, use Indexes on the appropriate field which you are querying.
https://docs.mongodb.com/manual/indexes/
Related
I run MongoDB 4.0 on WiredTiger under Ubuntu Server 16.04 to store complex documents. There is an issue with one of the collections: the documents have many images written as strings in base64. I understand this is a bad practice, but I need some time to fix it.
Because of this some find operations fail, but only those which have a non-empty filter or skip. For example db.collection('collection').find({}) runs OK while db.collection('collection').find({category: 1}) just closes connection after a timeout. It doesn't matter how many documents should be returned: if there's a filter, the error will pop every time (even if it should return 0 docs), while an empty query always executes well until skip is too big.
UPD: some skip values make queries to fail. db.collection('collection').find({}).skip(5000).limit(1) runs well, db.collection('collection').find({}).skip(9000).limit(1) takes way much time but executes too, while db.collection('collection').find({}).skip(10000).limit(1) fails every time. Looks like there's some kind of buffer where the DB stores query related data and on the 10000 docs it runs out of the resources. The collection itself has ~10500 docs. Also, searching by _id runs OK. Unfortunately, I have no opportunity to make new indexes because the operation fails just like read.
What temporary solution I may use before removing base64 images from the collection?
This happens because such a problematic data scheme causes huge RAM usage. The more entities there are in the collection, the more RAM is needed not only to perform well but even to run find.
Increasing MongoDB default RAM usage with storage.wiredTiger.engineConfig.cacheSizeGB config option allowed all the operations to run fine.
I understand that mongo db does locking on read and write operations.
My Use case:
Only read operations. No write operations.
I have a collection about 10million documents. Storage engine is wiredTiger.
Mongo version is 3.4.
I made a request which should return 30k documents - took 650ms on an average.
When I made concurrent requests - same requests - 100 times - It takes in seconds - few seconds to 2 minutes all requests handled.
I have single node to serve the data.
How do I access the data:
Each document contains 25 to 40 fields. I indexed few fields. I query based on one index field.
API will return all the matching documents in json form.
Other informations: API is written using Spring boot.
Concurrency tested through JMeter shell script from command line on remote machine.
So,
My question:
Am I missing any optimizations? [storage engine level, version]
Can't I achieve all read requests to be served less than a second?
If so, what sla I can keep for this use case?
Any suggestions?
Edit:
I enabled database profiler in mongodb with level 2.
My single query internally converted to 4 queries:
Initial read
getMore
getMore
getMore
These are the queries found through profiler.
Totally, it is taking less than 100ms. Is it true really?
My concurrent queries:
Now, When I hit 100 requests, nearly 150 operations are more than 100ms, 100 operations are more than 200ms, 90 operations are more than 300ms.
As per my single query analysis, 100 requests will be converted to 400 queries internally. It is fixed pattern which I verified by checking the query tag in the profiler output.
I hope this is what affects my request performance.
My single query internally converted to 4 queries:
Initial read
getMore
getMore
getMore
It's the way mongo cursors work. The documents are transferred from the db to the app in batches. IIRC the first batch is around 100 documents + cursor Id, then consecutive getMore calls retrieve next batches by cursor Id.
You can define batch size (number of documents in the batch) from the application. The batch cannot exceed 16MB, e.g. if you set batch size 30,000 it will fit into single batch only if document size is less than 500B.
Your investigation clearly show performance degradation under load. There are too many factors and I believe locking is not one of them. WiredTiger does exclusive locks on document level for regular write operations and you are doing only reads during your tests, aren't you? In any doubts you can compare results of db.serverStatus().locks before and after tests to see how many write locks were acquired. You can also run db.serverStatus().globalLock during the tests to check the queue. More details about locking and concurrency are there: https://docs.mongodb.com/manual/faq/concurrency/#for-wiredtiger
The bottleneck is likely somewhere else. There are few generic things to check:
Query optimisation. Ensure you use indexes. The profiler should have no "COLLSCAN" stage in execStats field.
System load. If your database shares system resources with application it may affect performance of the database. E.g. BSON to JSON conversion in your API is quite CPU hungry and may affect performance of the queries. Check system's LA with top or htop on *nix systems.
Mongodb resources. Use mongostat and mongotop if the server has enough RAM, IO, file descriptors, connections etc.
If you cannot spot anything obvious I'd recommend you to seek professional help. I find the simplest way to get one is by exporting data to Atlas, running your tests against the cluster. Then you can talk to the support team if they could advice any improvements to the queries.
I would like to know how long a mongo internal cache would sustain. I have a scenario in which i have some one million records and i have to perform a search on them using the mongo-java driver.
The initial search takes a lot of time (nearly one minute) where as the consecutive searches of same query reduces the computation time (to few seconds) due to mongo's internal caching mechanism.
But I do not know how long this cache would sustain, like is it until the system reboots or until the collection undergoes any write operation or things like that.
Any help in understanding this is appreciated!
PS:
Regarding the fields with which search is performed, some are indexed
and some are not.
Mongo version used 2.6.1
It will depend on a lot of factors, but the most prominent are the amount of memory in the server and how active the server is as MongoDB leaves much of the caching to the OS (by MMAP'ing files).
You need to take a long hard look at your log files for the initial query and try to figure out why it takes nearly a minute.
In most cases there is some internal cache invalidation mechanism that will drop your cached query internal record when write operation occurs. It is the simplest describing of process. Just from my own expirience.
But, as mentioned earlier, there are many factors besides simple invalidation that can have place.
MongoDB automatically uses all free memory on the machine as its cache.It would be better to use MongoDB 3.0+ versions because it comes with two Storage Engines MMAP and WiredTiger.
The major difference between these two is that whenever you perform a write operation in MMAP then the whole database is going to lock and whereas the locking mechanism is upto document level in WiredTiger.
If you are using MongoDB 2.6 version then you can also check the query performance and execution time taking to execute the query by explain() method and in version 3.0+ executionStats() in DB Shell Commands.
You need to index on a particular field which you will query to get results faster. A single collection cannot have more than 64 indexes. The more index you use in a collection there is performance impact in write/update operations.
db.collection.find in my app that uses mongodb java driver (latest) are super slow. I investigated one of them as follows
// about 300 hundred ids at a time (i've tried lower and higher numbers - no impact
db.users.find({_id : {$in : [1,2,3,4,5,6....]}})
Once I get the cursor I do: cursor.toArray() and then iterate of the results
The toArray operation is extremely slow. On average they take about a minute. IMPORTANT: my database is under very heavy load at all times. This particular collection has over 50mm entries.
I've narrowed down the issue in mongo java driver to com.mongodb.Response - specifically to this line:
final byte [] b = new byte[36];
Bits.readFully(in, b);
Incredibly readFully of just 36 bytes takes over a minute some times!
When I bring own the load on the databases, the improvements are drastic. From about a minute to 5-6 seconds. I mean 5-6 seconds to get 300 documents is still super slow, but definitely better then 1 minute.
What can I do to troubleshoot this further? Are there settings on MondoDB that I need to look at?
What happens
You are loading all of the 300 user documents.
What happens is that the _id index is searched and the respective documents are sent completely to your app. So mongoDB will access it's data files, read the first document and send it to you, then it jumps to the next document and sends it to you and so forth. If you used the cursor, you could start iterating over the returned documents as soon as a number of documents equalling your defined cursor size have been returned, as others will be lazily loaded from the cursor on the server on demand. (Bit of a simplification, but sufficient for answering this question). What you do is to explicitly wait until the index is scanned, the documents are located, sent back to your app and have reached it down to the last byte of the last document. As #wdberkeley (who works for 10gen) correctly pointed out, this is a Very Bad Idea™.
What might cause or intensify the problem
Under heavy load, two things might happen. The more likely is that your _id index isn't in RAM any more, causing thousands, if not millions of reads from disk - which is slow. Much slower than if the indices are kept in RAM (by several orders of magnitude). So it is not the code snippet you mentioned, but the response time of MongoDB which causes the delay. Another option under heavy load is that your disk IO is simply too low or (more likely) the random file read latency is too high. I assume you are using spinning disks plus not enough RAM for a database that size.
What to do to find the cause
Try to find out your index size using the db.users.stats(). I am pretty sure that your index size(s combined) exceed your available RAM.
Measure the disk IO and latency. If you use a GNU/Linux OS, you might want to find out how high your IOwait percentage is. A high percentage shows that your disk latency is too high for the load put on the server. It might even be that your are reaching the disk's IO limits.
Do your queries on a mongo shell. In case they are fast, you can be pretty sure that your toArray call is the cause of the problem.
What to do to resolve the problem
If you have not enough RAM, either scale up or scale out.
If your disk latency or throughput is too high, either scale out or ( better and cheaper in most cases ) use SSDs for storing MongoDB's data.
Use a cursor object to iterate over the documents. This is a better solution in almost every use case I can think of.
Upgrading MongoDB driver to 3.6.4 will fetch the data in no-time.
We have around 2 million documents in our collection and with previous version it was taking around ~3 minutes but after after upgrading to 3.6.4 it took only 5-7 sec.So what I feel is that there is some issue with the old version of mongoDB driver.
I run a single mongodb instance which is getting inserted with logs from an app server. the current rate of insert in production is 10 inserts per second. And its a capped collection. i DONT USE ANY INDEXES . Queries were running faster when there were small number of records. only one collection has that amount of data. even querying from collection that has very few rows has become very slow. IS there any means to improve the performance.
-Avinash
This is a very difficult question to answer because we dont know much about your configuration or your document structure.
One thing that immediately pops into my head is that you are running out of memory. 10 inserts per second doesn't mean much because we do not know how big the inserted documents are.
If you are inserting larger documents at 10 per second, you could be eating up memory, causing the operating system to push some of your records to disk.
When you query without using an index, you are forced to scan every document. If your documents have been pushed to disk by the OS, you will begin having page faults. Mongo will need to fetch pages of data off the hard disk, and load them into memory so that they can be scanned. Before doing this, the operating system will need to make room for that data in memory by flushing other parts of memory out to disk.
It sounds like you are are I/O bound and the two biggest things you can do to fix this are
Add more memory to the machine running mongod
Start using indexes so that the database does not need to do full collection scans
Use proper indexes, though that will have some effect on the efficiency of insertion in a capped collection.
It would be better if you can share the collection structure and the query you are using.