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Just start the mongod instance that you had stopped :)


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All things being equal, it is logical to assume that better hardware = better performance. However, computers are strange beasts - I know that with Oracle, if you assign very large sizes (you need good hardware) to certain caches, you can actually slow down the machine. The only real suggestion that I have is that you test, test and test again. You cannot ...


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Here is the mongodb documentation on how to covert replica set to a sharded cluster. http://docs.mongodb.org/manual/tutorial/convert-replica-set-to-replicated-shard-cluster/ You can follow these steps except creating and adding replica set/sets you can add your standalone instance to the sharded cluster.


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I ran into a similar issue and found it to be a misconfigured mongod.conf file in my case. It could also be the permissions on the new directory are not set properly. chown -R mongod:mongod <directory name> was how I ensured access (and of course the chmod 600 <dir> as well). Lastly, run an ls -Z to ensure the context is correct. I just ...


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You don't ever need to connect to specific shards in a MongoDB database. Instead, you connect to a mongos instance that handles the routing for you. In your case, you would connect to the mongos instance normally, by typing mongo into the terminal, or through a language specific client. You send your aggregation operation to the mongos instance, and it will ...


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You can find it through db.serverStatus() on section storageEngine - http://docs.mongodb.org/manual/reference/command/serverStatus/#storageengine


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Indices can speed up an aggregation when utilized in a $match or a $sort stage at the beginning of a pipeline. With your example, no index will be used, since you don't use either. So every document will be accessed.


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Since your error is: Failed to restart mongod.service: Unit mongod.service failed to load: No such file or directory. This is pretty much saying that your startup path is not in the expected location or else the Ubuntu install has led to problems. See: http://docs.mongodb.org/manual/tutorial/manage-mongodb-processes/ EDIT: Updated with link to what ...


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You may need to elaborate on this a bit more but yes it is possible to store that data as you've described it. Here's an example, using your document model, of such an array of dates in a document in a collection. Personally, I would look at modifying this as I've described at the end but of course it all depends on what your queries need to do with the ...


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After eliminating other memory-hungry processes, the machine runs smoothly at the moment. No such log entries like described above are observed. Thanks for pointing out that MongoDB really needs an amount of memory. Best


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For querying secondary run the following command first. After running this, you can do all regular stuff on secondary. rs.slaveOk() To see the replication status, run the following command on primary. This will show you if the secondaries are up to date or lagging. db.printSlaveReplicationInfo() Hope this helps.


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MongoDB does not currently support a multi-master setup such as you describe. If you were looking to provide localized regional writes via MongoDB, your best bet is to setup a sharded collection, using shard tag ranges. You would have 1 (or more) shard per region, with the primary residing within the region it manages and remote secondaries. Region would ...


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Nothing bad happened even if you took the config server offline during a chunk migration. In order for a chunk to be marked as migrated, all three config servers need to be up (Contrary to popular belief they do not form a replica set.). (The following is a tiny bit simplified for the sake of shortness.) When a chunk is moved, a global (read cluster wide) ...


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tl:dr The answer to your question is that you need to remodel your data for MongoDB to suit your use cases and write some ETL routine for migrating the data into a proper model which makes use of MongoDBs advantages. On automatic conversion tools Generally, using these tools is a bad idea. Since a tool has no idea what your use cases (and subsequently ...


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3 different things. Hadoop is a framework. Something you use to develop an entire application. MongoDb is a db. A nosql one. It is where you store data. BigData is a concept. It is related to huge quantity of data. Where huge is not a fixed parameter. 4 mb was huge in 1960. Now huge means several terabyte.


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There is a tool on CodePlex you can use for this, called MSSQL to MongoDB Tool. This tool will migrate all data from MSSQL to MongoDB. It will do this without exporting to a file first. If you want to export to file first, you are best off using the Export Data wizard which you can find in your SSMS, by right clicking your DB and choose 'Export Data' under ...


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An important consideration with any benchmarking is that you make sure that you are testing the right thing (i.e. the bottlenecks really are where you think that they are). In this instance the code that is performing your inserts may be single-threaded and synchronous meaning that is is going to wait for each item to hit the data store before starting next. ...



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