Home Big Data 5 Duties to Automate Utilizing Scheduled Question Lambdas in Rockset

5 Duties to Automate Utilizing Scheduled Question Lambdas in Rockset

5 Duties to Automate Utilizing Scheduled Question Lambdas in Rockset


Why and what to automate

As software builders and designers, every time we see repeating duties, we instantly take into consideration find out how to automate them. This simplifies our day by day work and permits us to be extra environment friendly and centered on delivering worth to the enterprise.


Typical examples of repeating duties embrace scaling compute assets to optimize their utilization from a price and efficiency perspective, sending automated e-mails or Slack messages with outcomes of a SQL question, materializing views or doing periodic copies of knowledge for improvement functions, exporting knowledge to S3 buckets, and so forth.

How Rockset helps with automation

Rockset presents a set of highly effective options to assist automate widespread duties in constructing and managing knowledge options:

  • a wealthy set of APIs so that each facet of the platform could be managed by means of REST
  • Question Lambdas – that are REST API wrappers round your parametrized SQL queries, hosted on Rockset
  • scheduling of Question Lambdas – a not too long ago launched function the place you possibly can create schedules for automated execution of your question lambdas and put up outcomes of these queries to webhooks
  • compute-compute separation (together with a shared storage layer) which permits isolation and impartial scaling of compute assets

Let’s deep dive into why these are useful for automation.

Rockset APIs let you work together with all your assets – from creating integrations and collections, to creating digital situations, resizing, pausing and resuming them, to operating question lambdas and plain SQL queries.

Question Lambdas provide a pleasant and simple to make use of method to decouple customers of knowledge from the underlying SQL queries with the intention to maintain what you are promoting logic in a single place with full supply management, versioning and internet hosting on Rockset.

Scheduled execution of question lambdas lets you create cron schedules that may robotically execute question lambdas and optionally put up the outcomes of these queries to webhooks. These webhooks could be hosted externally to Rockset (to additional automate your workflow, for instance to put in writing knowledge again to a supply system or ship an e-mail), however you can too name Rockset APIs and carry out duties like digital occasion resizing and even creating or resuming a digital occasion.

Compute-compute separation means that you can have devoted, remoted compute assets (digital situations) per use case. This implies you possibly can independently scale and dimension your ingestion VI and a number of secondary VIs which might be used for querying knowledge. Rockset is the primary real-time analytics database to supply this function.

With the mix of those options, you possibly can automate the whole lot you want (besides perhaps brewing your espresso)!

Typical use circumstances for automation

Let’s now have a look into typical use circumstances for automation and present how you’d implement them in Rockset.

Use case 1: Sending automated alerts

Typically occasions, there are necessities to ship automated alerts all through the day with outcomes of SQL queries. These could be both enterprise associated (like widespread KPIs that the enterprise is concerned about) or extra technical (like discovering out what number of queries ran slower than 3 seconds).

Utilizing scheduled question lambdas, we are able to run a SQL question towards Rockset and put up the outcomes of that question to an exterior endpoint corresponding to an e-mail supplier or Slack.

Let’s have a look at an e-commerce instance. We have now a set known as ShopEvents with uncooked real-time occasions from a webshop. Right here we monitor each click on to each product in our webshop, after which ingest this knowledge into Rockset by way of Confluent Cloud. We’re concerned about figuring out what number of objects have been bought on our webshop in the present day and we wish to ship this knowledge by way of e-mail to our enterprise customers each six hours.


We’ll create a question lambda with the next SQL question on our ShopEvents assortment:

    COUNT(*) As ItemsSold
    Timestamp >= CURRENT_DATE() AND EventType="Checkout";

We’ll then use SendGrid to ship an e-mail with the outcomes of that question. We gained’t undergo the steps of establishing SendGrid, you possibly can observe that in their documentation.

When you’ve received an API key from SendGrid, you possibly can create a schedule in your question lambda like this, with a cron schedule of 0 */6 * * * for each 6 hours:


This can name the SendGrid REST API each 6 hours and can set off sending an e-mail with the full variety of bought objects that day.

{{QUERY_ID}} and {{QUERY_RESULTS}} are template values that Rockset offers robotically for scheduled question lambdas with the intention to use the ID of the question and the ensuing dataset in your webhook calls. On this case, we’re solely within the question outcomes.

After enabling this schedule, that is what you’ll get in your inbox:


You can do the identical with Slack API or another supplier that accepts POST requests and Authorization headers and also you’ve received your automated alerts arrange!

Should you’re concerned about sending alerts for gradual queries, have a look at establishing Question Logs the place you possibly can see an inventory of historic queries and their efficiency.

Use case 2: Creating materialized views or improvement datasets

Rockset helps automated real-time rollups on ingestion for some knowledge sources. Nonetheless, if in case you have a must create further materialized views with extra complicated logic or if you’ll want to have a replica of your knowledge for different functions (like archival, improvement of recent options, and so on.), you are able to do it periodically by utilizing an INSERT INTO scheduled question lambda. INSERT INTO is a pleasant method to insert the outcomes of a SQL question into an current assortment (it could possibly be the identical assortment or a very totally different one).

Let’s once more have a look at our e-commerce instance. We have now a knowledge retention coverage set on our ShopEvents assortment in order that occasions which might be older than 12 months robotically get faraway from Rockset.


Nonetheless, for gross sales analytics functions, we wish to make a copy of particular occasions, the place the occasion was a product order. For this, we’ll create a brand new assortment known as OrdersAnalytics with none knowledge retention coverage. We’ll then periodically insert knowledge into this assortment from the uncooked occasions assortment earlier than the info will get purged.


We will do that by making a SQL question that may get all Checkout occasions for the day past:

INSERT INTO "Demo-Ecommerce".OrdersAnalytics
    e.EventId AS _id,
    "Demo-Ecommerce".ShopEvents e
    AND e.EventType="Checkout";

Observe the _id area we’re utilizing on this question – this can be certain that we don’t get any duplicates in our orders assortment. Take a look at how Rockset robotically handles upserts right here.

Then we create a question lambda with this SQL question syntax, and create a schedule to run this as soon as a day at 1 AM, with a cron schedule 0 1 * * *. We don’t must do something with a webhook, so this a part of the schedule definition is empty.


That’s it – now we’ll have day by day product orders saved in our OrdersAnalytics assortment, prepared to be used.

Use case 3: Periodic exporting of knowledge to S3

You should use scheduled question lambdas to periodically execute a SQL question and export the outcomes of that question to a vacation spot of your alternative, corresponding to an S3 bucket. That is helpful for eventualities the place you’ll want to export knowledge regularly, corresponding to backing up knowledge, creating stories or feeding knowledge into downstream programs.

On this instance, we’ll once more work on our e-commerce dataset and we’ll leverage AWS API Gateway to create a webhook that our question lambda can name to export the outcomes of a question into an S3 bucket.


Much like our earlier instance, we’ll write a SQL question to get all occasions from the day past, be a part of that with product metadata and we’ll save this question as a question lambda. That is the dataset we wish to periodically export to S3.

    "Demo-Ecommerce".ShopEvents e
    INNER JOIN "Demo-Ecommerce".Merchandise p ON e.EventDetails.ProductID = p._id

Subsequent, we’ll must create an S3 bucket and arrange AWS API Gateway with an IAM Function and Coverage in order that the API gateway can write knowledge to S3. On this weblog, we’ll deal with the API gateway half – be sure you examine the AWS documentation on find out how to create an S3 bucket and the IAM position and coverage.

Observe these steps to arrange AWS API Gateway so it’s prepared to speak with our scheduled question lambda:

  1. Create a REST API software within the AWS API Gateway service, we are able to name it rockset_export:


  1. Create a brand new useful resource which our question lambdas will use, we’ll name it webhook:


  1. Create a brand new POST methodology utilizing the settings under – this primarily allows our endpoint to speak with an S3 bucket known as rockset_export:


  • AWS Area: Area in your S3 bucket
  • AWS Service: Easy Storage Service (S3)
  • HTTP methodology: PUT
  • Motion Kind: Use path override
  • Path override (elective): rockset_export/{question _id} (exchange along with your bucket identify)
  • Execution position: arn:awsiam::###:position/rockset_export (exchange along with your ARN position)
  • Setup URL Path Parameters and Mapping Templates for the Integration Request – this can extract a parameter known as query_id from the physique of the incoming request (we’ll use this as a reputation for information saved to S3) and query_results which we’ll use for the contents of the file (that is the results of our question lambda):


As soon as that’s performed, we are able to deploy our API Gateway to a Stage and we’re now able to name this endpoint from our scheduled question lambda.

Let’s now configure the schedule for our question lambda. We will use a cron schedule 0 2 * * * in order that our question lambda runs at 2 AM within the morning and produces the dataset we have to export. We’ll name the webhook we created within the earlier steps, and we’ll provide query_id and query_results as parameters within the physique of the POST request:


We’re utilizing {{QUERY_ID}} and {{QUERY_RESULTS}} within the payload configuration and passing them to the API Gateway which can use them when exporting to S3 because the identify of the file (the ID of the question) and its contents (the results of the question), as described in step 4 above.

As soon as we save this schedule, we now have an automatic job that runs each morning at 2 AM, grabs a snapshot of our knowledge and sends it to an API Gateway webhook which exports this to an S3 bucket.

Use case 4: Scheduled resizing of digital situations

Rockset has help for auto-scaling digital situations, but when your workload has predictable or effectively understood utilization patterns, you possibly can profit from scaling your compute assets up or down based mostly on a set schedule.

That method, you possibly can optimize each spend (so that you just don’t over-provision assets) and efficiency (so that you’re prepared with extra compute energy when your customers wish to use the system).

An instance could possibly be a B2B use case the place your clients work primarily in enterprise hours, let’s say 9 AM to five PM all through the work days, and so that you want extra compute assets throughout these occasions.

To deal with this use case, you possibly can create a scheduled question lambda that may name Rockset’s digital occasion endpoint and scale it up and down based mostly on a cron schedule.


Observe these steps:

  1. Create a question lambda with only a choose 1 question, since we don’t really need any particular knowledge for this to work.
  2. Create a schedule for this question lambda. In our case, we wish to execute as soon as a day at 9 AM so our cron schedule might be 0 9 * * * and we’ll set limitless variety of executions in order that it runs day by day indefinitely.
  3. We’ll name the replace digital occasion webhook for the particular VI that we wish to scale up. We have to provide the digital occasion ID within the webhook URL, the authentication header with the API key (it wants permissions to edit the VI) and the parameter with the NEW_SIZE set to one thing like MEDIUM or LARGE within the physique of the request.


We will repeat steps 1-3 to create a brand new schedule for scaling the VI down, altering the cron schedule to one thing like 5 PM and utilizing a smaller dimension for the NEW_SIZE parameter.

Use case 5: Establishing knowledge analyst environments

With Rockset’s compute-compute separation, it’s straightforward to spin up devoted, remoted and scalable environments in your advert hoc knowledge evaluation. Every use case can have its personal digital occasion, guaranteeing {that a} manufacturing workload stays secure and performant, with the most effective price-performance for that workload.

On this situation, let’s assume we now have knowledge analysts or knowledge scientists who wish to run advert hoc SQL queries to discover knowledge and work on numerous knowledge fashions as a part of a brand new function the enterprise needs to roll out. They want entry to collections and so they want compute assets however we don’t need them to create or scale these assets on their very own.

To cater to this requirement, we are able to create a brand new digital occasion devoted to knowledge analysts, be certain that they’ll’t edit or create VIs by making a customized RBAC position and assign analysts to that position, and we are able to then create a scheduled question lambda that may resume the digital occasion each morning in order that knowledge analysts have an setting prepared once they log into the Rockset console. We may even couple this with use case 2 and create a day by day snapshot of manufacturing right into a separate assortment and have the analysts work on that dataset from their digital occasion.


The steps for this use case are much like the one the place we scale the VIs up and down:

  1. Create a question lambda with only a choose 1 question, since we don’t really need any particular knowledge for this to work.
  2. Create a schedule for this question lambda, let’s say day by day at 8 AM Monday to Friday and we’ll restrict it to 10 executions as a result of we wish this to solely work within the subsequent 2 working weeks. Our cron schedule might be 0 8 * * 1-5.
  3. We’ll name the resume VI endpoint. We have to provide the digital occasion ID within the webhook URL, the authentication header with the API key (it wants permissions to renew the VI). We don’t want any parameters within the physique of the request.


That’s it! Now we now have a working setting for our knowledge analysts and knowledge scientists that’s up and operating for them each work day at 8 AM. We will edit the VI to both auto-suspend after sure variety of hours or we are able to have one other scheduled execution which can droop the VIs at a set schedule.

As demonstrated above, Rockset presents a set of helpful options to automate widespread duties in constructing and sustaining knowledge options. The wealthy set of APIs mixed with the ability of question lambdas and scheduling let you implement and automate workflows which might be fully hosted and operating in Rockset so that you just don’t need to depend on third get together elements or arrange infrastructure to automate repeating duties.

We hope this weblog gave you just a few concepts on find out how to do automation in Rockset. Give this a attempt to tell us the way it works!



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