I have a requirement to load 100's of tables to BigQuery from Google Cloud Storage(GCS -> Temp table -> Main table). I have created a python process to load the data into BigQuery and scheduled in AppEngine. Since we have Maximum 10min timeout for AppEngine. I have submitted the jobs in Asynchronous mode and checking the job status later point of time. Since I have 100's of tables need to create a monitoring system to check the status the job load.
Need to maintain a couple of tables and bunch of views to check the job status.
The operational process is little complex. Is there any better way?
Thanks
When we did this, we simply used a message queue like Beanstalkd, where we pushed something that later had to be checked, and we wrote a small worker who subscribed to the channel and dealt with the task.
On the other hand: BigQuery offers support for querying data directly from Google Cloud Storage.
Use cases:
- Loading and cleaning your data in one pass by querying the data from a federated data source (a location external to BigQuery) and writing the cleaned result into BigQuery storage.
- Having a small amount of frequently changing data that you join with other tables. As a federated data source, the frequently changing data does not need to be reloaded every time it is updated.
https://cloud.google.com/bigquery/federated-data-sources
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In our application we currently use dynamoDb to store the notification details. So a scheduler runs twice a day which queries "notificationType"(pk -> notifiactionType, sk -> userId).
In each item there is an attribute(timestamp), based on which if the timestamp is more than the current time will send a trigger(more business logic that for some records one day after the timestamp a mail needs to be sent). Now once the user performs the activity for which the notification is sent, then will delete the entry
My query is that, if the data grows large for a notificationType, then retrieval of all the data is redundant because for some records the notification is not going to be sent. Hence more read capacity is used and that might potentially increase the cost in later point of time.
In this case would it be wise to use the existing dynamoDb or move to any other db like mongoDb, cassandra or any other db.
Note: My primary concern is the cost
Another option is to use a workflow engine that can model the notification process per user instead of a batch job. This way you can avoid scanning large amounts of data as the engine would rely on durable timers to execute actions at the appropriate time.
My open-source project temporal.io which I led at Uber is used by multiple companies for notification like scenarios and was tested up 200 million open parallel workflows.
We have an application running on Google App Engine using Datastore as persistence back-end. Currently application has mostly 'OLTP' features and some rudimentary reporting. While implementing reports we experienced that processing large amount of data (millions of objects) is very difficult using Datastore and GQL. To enhance our application with proper reports and Business Intelligence features we think its better to setup a ETL process to move data from Datastore to BigQuery.
Initially we thought of implementing the ETL process as App Engine cron job but it looks like Dataflow can also be used for this. We have following requirements for setting up the process
Be able to push all existing data to BigQuery by using Non streaming
API of BigQuery.
Once above is done, push any new data whenever it is updated/created in
Datastore to BigQuery using streaming API.
My Questions are
Is Cloud Dataflow right candidate for implementing this pipeline?
Will we be able to push existing data? Some of the Kinds have
millions of objects.
What should be the right approach to implement it? We are considering two approaches.
First approach is to go through pub/sub i.e. for existing data create a cron job and push all data to pub/sub. For any new updates push data to pub/sub at the same time it is updated in DataStore. Dataflow Pipeline will pick it from pub/sub and push it to BigQuery.
Second approach is to create a batch Pipeline in Dataflow that will query DataStore and pushes any new data to BigQuery.
Question is are these two approaches doable? which one is better cost wise? Is there any other way which is better than above two?
Thank you,
rizTaak
Dataflow can absolutely be used for this purpose. In fact, Dataflow's scalability should make the process fast and relatively easy.
Both of your approaches should work -- I'd give a preference to the second one of using a batch pipeline to move the existing data, and then a streaming pipeline to handle new data via Cloud Pub/Sub. In addition to the data movement, Dataflow allow arbitrary analytics/manipulation to be performed on the data itself.
That said, BigQuery and Datastore can be connected directly. See, for example, Loading Data From Cloud Datastore in BigQuery documentation.
I am investigating what might be the best infrastructure for storing log files from many clients.
Google App engine offers a nice solution that doesn't make the process a IT nightmare: Load balancing, sharding, server, user authentication - all in once place with almost zero configuration.
However, I wonder if the Datastore model is the right for storing logs. Each log entry should be saved as a single document, where each clients uploads its document on a daily basis and can consists of 100K of log entries each day.
Plus, there are some limitation and questions that can break the requirements:
60 seconds timeout on bulk transaction - How many log entries per second will I be able to insert? If 100K won't fit into the 60 seconds frame - this will affect the design and the work that needs to be put into the server.
5 inserts per entity per seconds - Is a transaction considered a single insert?
Post analysis - text search, searching for similar log entries cross clients. How flexible and efficient is Datastore with these queries?
Real time data fetch - getting all the recent log entries.
The other option is to deploy an elasticsearch cluster on goole compute and write the server on our own which fetches data from ES.
Thanks!
Bad idea to use datastore and even worse if you use entity groups with parent/child as a comment mentions when comparing performance.
Those numbers do not apply but datastore is not at all designed for what you want.
bigquery is what you want. its designed for this specially if you later want to analyze the logs in a sql-like fashion. Any more detail requires that you ask a specific question as it seems you havent read much about either service.
I do not agree, Data Store is a totally fully managed no sql document store database, you can store the logs you want in this type of storage and you can query directly in datastore, the benefits of using this instead of BigQuery is the schemaless part, in BigQuery you have to define the schema before inserting the logs, this is not necessary if you use DataStore, think of DataStore as a MongoDB log analysis use case in Google Cloud.
To move data from datastore to bigquery tables I currently follow a manual and time consuming process, that is, backing up to google cloud storage and restoring to bigquery. There is scant documentation on the restoring part so this post is handy http://sookocheff.com/posts/2014-08-04-restoring-an-app-engine-backup/
Now, there is a seemingly outdated article (with code) to do it https://cloud.google.com/bigquery/articles/datastoretobigquery
I've been, however, waiting for access to this experimental tester program that seems to automate the process, but gotten no access for months https://docs.google.com/forms/d/1HpC2B1HmtYv_PuHPsUGz_Odq0Nb43_6ySfaVJufEJTc/viewform?formkey=dHdpeXlmRlZCNWlYSE9BcE5jc2NYOUE6MQ
For some entities, I'd like to push the data to big query as it comes (inserts and possibly updates). For more like biz intelligence type of analysis, a daily push is fine.
So, what's the best way to do it?
There are three ways of entering data into bigquery:
through the UI
through the command line
via API
If you choose API, then you can have two different ways: "batch" mode or streaming API.
If you want to send data "as it comes" then you need to use the streaming API. Every time you detect a change on your datastore (or maybe once every few minutes, depending on your needs), you have to call the insertAll method of the API. Please notice you need to have a table created beforehand with the structure of your datastore. (This can be done via API if needed too).
For your second requirement, ingesting data once a day, you have the full code in the link you provided. All you need to do is adjust the JSON schema to those of your data store and you should be good to do.
I have an AppEngine application that process files from Cloud Storage and inserts them in BigQuery.
Because now and also in the future I would like to know the sanity/performance of the application... I would like to store stats data in either Cloud Datastore or in a Cloud SQL instance.
I have two questions I would like to ask:
Cloud Datastore vs Cloud SQL - what would you use and why? What downsides have you experienced so far?
Would you use a task or direct call to insert data and, also, why? - Would you add a task and then have some consumers insert to data or would you do a direct insert [ regardless of the solution choosen above ]. What downsides have you experienced so far?
Thank you.
Cloud SQL is better if you want to perform JOINs or SUMs later, Cloud Datastore will scale more if you have a lot of data to store. Also, in the Datastore, if you want to update a stats entity transactionally, you will need to shard or you will be limited to 5 updates per second.
If the data to insert is small (one row to insert in BQ or one entity in the datastore) then you can do it by a direct call, but you must accept that the call may fail. If you want to retry in case of failure, or if the data to insert is big and it will take time, it is better to run it asynchronously in a task. Note that with tasks,y you must be cautious because they can be run more than once.