Suppose that I have a huge SQLite file (say, 500[MB]) stored in Amazon S3.
Can a python script that is run on a small EC2 instance directly access and modify that SQLite file? or must I first copy the file to the EC2 instance, change it there and then copy over to S3?
Will the I/O be efficient?
Here's what I am trying to do. As I wrote, I have a 500[MB] SQLite file in S3. I'd like to start say 10 different Amazon EC2 instances that will each read a subset of the file and do some processing (every instance will handle a different subset of the 500[MB] SQLite file). Then, once processing is done, every instance will update only the subset of the data it dealt with (as explained, there will be no overlap of data among processes).
For example, suppose that the SQLite file has say 1M rows:
instance 1 will deal with (and update) rows 0 - 100000
instance 2 will will deal with (and update) rows 100001 - 200000
.........................
instance 10 will deal with (and update) rows 900001 - 1000000
Is it at all possible? Does it sound OK? any suggestions / ideas are welcome.
I'd like to start say 10 different Amazon EC2 instances that will each read a subset of the file and do some processing (every instance will handle a different subset of the 500[MB] SQLite file)
You cannot do this with SQLite; on amazon infrastructure or otherwise. sqlite performs database level write locking. unless all ten nodes are performing reads exclusively, you will not attain any kind of concurrency. Even the SQLite website says so.
Situations Where Another RDBMS May Work Better
Client/Server Applications
High-volume Websites
Very large datasets
High Concurrency
Have you considered PostgreSQL?
Since S3 cannot be directly mounted, your best bet is to create an EBS volume containing the SQLite file and work directly with the EBS volume from another (controller) instance. You can then create snapshots of the volume, and archive it into S3. Using a tool like boto (Python API), you can automate the creation of snapshots and the process of moving the backups into S3.
You can mount S3 bucket on your linux machine. See below:
s3fs -
http://code.google.com/p/s3fs/wiki/InstallationNotes
- this did work for me. It uses FUSE file-system + rsync to sync the files
in S3. It kepes a copy of all
filenames in the local system & make
it look like a FILE/FOLDER.
This is good if the system is already in place and running with huge collection of data. But, if you are building this from scratch then I would suggest you to have an EBS volume for SQLite and use this script to create a snapshot of your EBS volume:
https://github.com/rakesh-sankar/Tools/blob/master/AmazonAWS/EBS/ebs-snapshot.sh
If your db structure is simple, why not just use AWS simpledb? Or run mysql (or another DB) on one of your instances.
Amazon EFS can be shared among ec2 instances. It's a managed NFS share. SQLITE will still lock the whole DB on write.
The SQLITE Website does not recommend NFS shares, though. But depending on the application you can share the DB read-only among several ec2 instances and store the results of your processing somewhere else, then concatenate the results in the next step.
Related
I joined as a junior data engineer at a startup and I'm working on setting up a data warehouse for BI/visualization. I wanted to get an idea of approaches for the extraction/loading part as the company is also new to data engineering.
The company is thinking of going with Google BigQuery for warehousing. The main data source is currently a single OLTP PostgreSQL database hosted on AWS RDS. The database is about 50 GB for now with nearly a hundred tables.
I was initially thinking of using Stitch to integrate directly with BigQuery but since the team is shifting the RDS instance to a private subnet, it would not be possible to access using third party tools which would require a publicly accessible URL (would it?).
How would I go about it? I am still pretty new to data engineering so wanted some advice. I was thinking about using:
RDS -> Lambda/VM with Python extraction/load script -> BigQuery upload using API
But how would I account for changing row values e.g. a customer's status changes in a table. Would BigQuery automatically handle such changes? Plus, I would want to set up regular daily data transfers. For this, I think a cron job can be set up with the Python script to transfer data but would this be a costly approach considering that there are a bunch of large tables (extraction, conversion to dataframe/CSV then uploading to BQ)? As the data size increases, I would need to upsert data instead of overwriting tables. Can BigQuery or other warehouse solutions like Redshift handle this? My main factors to consider for a solution are mostly cost, time to set up and data loading durations.
I am attempting to write a python script which will run in AWS Lambda, back up a PostgreSQL database table which is hosted in Amazon RDS, then dump a resulting .bak file or similar to S3.
I'm able to connect to the database and make changes to it, but I'm not quite sure how to go about the next steps. How do I actually back up the DB and write it to a backup file in the S3 bucket?
Depending how large you database is lambda may not be the best solution. lambdas have limits of 512MB tmp disk space, 15 minute timeouts, and 3008 MB memory. Maxing out these limits may also be more expensive then other options.
Using EC2 or fargate along with boto or the aws cli may be a better solution. Here is an blog entry that walks through a solution
https://francescoboffa.com/using-s3-to-store-your-mysql-or-postgresql-backups
The method that worked for me was to create an AWS data pipeline to back up the database to CSV.
I have about 100 million json files (10 TB), each with a particular field containing a bunch of text, for which I would like to perform a simple substring search and return the filenames of all the relevant json files. They're all currently stored on Google Cloud Storage. Normally for a smaller number of files I might just spin up a VM with many CPUs and run multiprocessing via Python, but alas this is a bit too much.
I want to avoid spending too much time setting up infrastructure like a Hadoop server, or loading all of that into some MongoDB database. My question is: what would be a quick and dirty way to perform this task? My original thoughts were to set up something on Kubernetes with some parallel processing running Python scripts, but I'm open to suggestions and don't really have a clue how to go about this.
Easier would be to just load the GCS data into Big Query and just run your query from there.
Send your data to AWS S3 and use Amazon Athena.
The Kubernetes option would be set up a cluster in GKE and install Presto in it with a lot of workers, use a hive metastore with GCS and query from there. (Presto doesn't have direct GCS connector yet, afaik) -- This option seems more elaborate.
Hope it helps!
I have a Python Scraper that I run periodically in my free tier AWS EC2 instance using Cron that outputs a csv file every day containing around 4-5000 rows with 8 columns. I have been ssh-ing into it from my home Ubuntu OS and adding the new data to a SQLite database which I can then use to extract the data I want.
Now I would like to try the free tier AWS MySQL database so I can have the database in the Cloud and pull data from it from my terminal on my home PC. I have searched around and found no direct tutorial on how this could be done. It would be great if anyone that has done this could give me a conceptual idea of the steps I would need to take. Ideally I would like to automate the updating of the database as soon as my EC2 instance updates with a new csv table. I can do all the de-duping once the table is in the aws MySQL database.
Any advice or link to tutorials on this most welcome. As I stated, I have searched quite a bit for guides but haven't found anything on this. Perhaps the concept is completely wrong and there is an entirely different way of doing it that I am not seeing?
The problem is you don't have access to RDS filesystem, therefore cannot upload csv there (and import too).
Modify your Python Scraper to connect to DB directly and insert data there.
Did you consider using AWS Lambda to run your scraper?
Take a look at this AWS tutorial which will help you configure a Lambda Function to access an Amazon RDS database.
I am trying to write a data migration script moving data from one database to another (Teradata to snowflake) using JDBC cursors.
The table I am working on has about 170 million records and I am running into the issue where when I execute the batch insert a maximum number of expressions in a list exceeded, expected at most 16,384, got 170,000,000.
I was wondering if there was any way around this or if there was a better way to batch migrate records without exporting the records to a file and moving it to s3 to be consumed by the snowflake.
If your table has 170M records, then using JDBC INSERT to Snowflake is not feasible. It would perform millions of separate insert commands to the database, each requiring a round-trip to the cloud service, which would require hundreds of hours.
Your most efficient strategy would be to export from Teradata into multiple delimited files -- say with 1 - 10 million rows each. You can then either use the Amazon's client API to move the files to S3 using parallelism, or use Snowflake's own PUT command to upload the files to Snowflake's staging area for your target table. Either way, you can then load the files very rapidly using Snowflake's COPY command once they are in your S3 bucket or Snowflake's staging area.