I want to keep a windows azure hdinsight cluster always running so that I can periodically write updates from my master data store (which is mongodb) and have it process map-reduce jobs on demand.
How can periodically sync data from mongodb with the hdinsight service? I'm trying to not have to upload all data whenever a new query is submitted which anytime, but instead have it somehow pre-warmed.
Is that possible on hdinsight? Is it even possible with hadoop?
Thanks,
It is certainly possible to have that data pushed from Mongo into Hadoop.
Unfortunately HDInsight does not support HBase (yet) otherwise you could use something like ZeroWing which is a solution from Stripe that reads the MongoDB Op log used by Mongo for replication and then writes that our to HBase.
Another solution might be to write out documents from your Mongo to Azure Blob storage, this means you wouldn't have to have the cluster up all the time, but would be able to use it to do periodic map reduce analytics against the files in the storage vault.
Your best method is undoubtedly to use the Mongo Hadoop connector. This can be installed in HDInsight, but it's a bit fiddly. I've blogged a method here.
Related
I believe that aws lambdas (serverless) are not good for batching, by definition they stop after 15 minutes of processing.
I have a Mongo Atlas (MongoDB Cloud Service) DB and I need to process a large dataset/collection several times a week, from a EC2 NodeJS app.
¿Which could be good architectural solutions for this to be efficient ?
Regards
Two key observations here. The size of the data and storage. MongoDB is partitioned and data can be read and processed in parallel.
For those both properties, Apache Spark is the best processing option. In AWS, couple of services provide this option and they are Amazon EMR and AWS Glue. From cost perspective and flexibility perspective, Amazon EMR is the best option.
What if you are not looking for parallel processing.? In that case, AWS Batch would be better option. Using AWS Batch you can run batch jobs with either EC2 or Fargate infra. You don't have to worry about provisioning and terminating the cluster with AWS Batch.
While searching for a service to migrate our on-premise MongoDB to Azure CosmosDB with Mongo API, We came across the service called, Azure Data Bricks. We have total of 186GB of data. which we need to migrate to CosmosDB with less downtime as possible. How can we improve the data transfer rate for that. If someone can give some insights to this spark based PaaS provided by Azure, It will be very much helpful.
Thank you
Have you referred the article given on our docs page?
In general you can assume the migration workload can consume entire provisioned throughput, the throughout provisioned would give an estimation of the migration speed. You could consider increasing the RUs at the time of migration and reduce it later.
The migration performance can be adjusted through these configurations:
Number of workers and cores in the Spark cluster
maxBatchSize
Disable indexes during data transfer
My current project have the following structure:
Starts with a script in jupyter notebook which dowloads data from a CRM API to put in a local PostgressSql database I run with PgAdmin. After that it runs cluster analysis, return some scoring values, creates a table in database with the results and updates this values in the CRM with another API call. This process will take between 10 to 20 hours (the API only allows 400 requests per minute).
The second notebook reads the database, detects last update, runs api call to update database since the last call, runs kmeans analysis to cluster the data, compare results with the previous call, updates the new ones and the CRM via API. This second process takes less than 2 hours in my estimation and I want this script to run every 24 hours.
After testing, this works fine. Now I'm evaluating how to put this in production in AWS. I understand for the notebooks I need Sagemaker and from I have seen is not that complicated, my only doubt here is if I can call the API without implementing aditional code or need some configuration. My second problem is database. I don't understand the difference between RDS which is the one I think I have to use for this and Aurora or S3. My goal is to write the less code as possible, but a have try some tutorial of RDS like this one: [1]: https://www.youtube.com/watch?v=6fDTre5gikg&t=10s, and I understand this connect my local postgress to AWS but I can't find the data in the amazon page, only creates an instance?? and how to connect to it to analysis this data from SageMaker. My final goal is to run the notebooks in the cloud and connect to my postgres in the cloud. Just some orientation about how to use this tools would be appreciated.
I don't understand the difference between RDS which is the one I think I have to use for this and Aurora or S3
RDS and Aurora are relational databases fully managed by AWS. "Regular" RDS allows you to launch the existing popular databases such as MySQL, PostgreSQSL and other which you can launch at home/work as well.
Aurora is in-house, cloud-native implementation databases compatible with MySQL and PosrgreSQL. It can store the same data as RDS MySQL or PosrgreSQL, but provides a number of features not available for RDS, such as more read replicas, distributed storage, global databases and more.
S3 is not a database, but an object storage, where you can store your files, such as images, csv, excels, similarly like you would store them on your computer.
I understand this connect my local postgress to AWS but I can't find the data in the amazon page, only creates an instance??
You can migrate your data from your local postgress to RDS or Aurora if you wish. But RDS nor Aurora will not connect to your existing local database, as they are databases themselfs.
My final goal is to run the notebooks in the cloud and connect to my postgres in the cloud.
I don't see a reason why you wouldn't be able to connect to the database. You can try to make it work, and if you encounter difficulties you can make new question on SO with RDS/Aurora setup details.
The idea here is, I have mongo cluster deployed in managed cloud service atlas. I have enabled Continuous Backup.
Now what I want to do is :
1) I want to use existing backup.
2) Using this existing backup I want to create similar cluster
(having same data form backup)
3) Automate this process so that every day my new cluster gets upto date from original cluster.
Note: The idea here for cloning cluster is, The original cluster is production data. I want to create a db which has similar data on which I can plug and play using any analytic tools and perform diffrent operations without affecting production data and load.
So far what I have found is to use mongorestore and mongodump.But here mongodump is putting load on production db even though my backup is enabled. I want to use same backup to clone this to another db cluster.
Deployed on Atlas, your server must have replica set.
Here are 2 solutions :
You need only reading data : connect your tools to a secondary server (ideally dedicated with priority 0 for becoming primary)
You need to read/write data : on the same server than above, play your mongodump command with --oplog option. By this way, you're dumping your data from a read-only server, preventing slowing performances of your main servers.
In this last case, what you need will find its solution in backup strategies, take a look at the doc to know more.
There's an offering for this purpose in ATLAS called analytic node.Link.
Analytic node is read replica of your database. Plus it will not interfere with your production traffic which makes it safer.
Also, you can connect BI connectors to this node and create your analytic platform.
We used redash.
I am deciding to use MongoDB as a Document management DB in my application. Initially I was thinking to use S3 as a data store but it seems mongoDB uses local file system to store data. Can I use S3 as data store in MongoDB.
thanx
Provisioned IOPS in AWS is ideal for MongoDB.
This link has notes about running MongoDB on AWS and is rather useful.