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 utility builders and designers, every time we see repeating duties, we instantly take into consideration how you can automate them. This simplifies our day by day work and permits us to be extra environment friendly and targeted 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 information for improvement functions, exporting information to S3 buckets, and so forth.

How Rockset helps with automation

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

  • a wealthy set of APIs so that each side of the platform may 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 characteristic the place you’ll be able to create schedules for computerized 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 can help you work together with your entire assets – from creating integrations and collections, to creating digital situations, resizing, pausing and resuming them, to working question lambdas and plain SQL queries.

Question Lambdas provide a pleasant and straightforward to make use of option to decouple shoppers of information from the underlying SQL queries so that you could hold your enterprise logic in a single place with full supply management, versioning and internet hosting on Rockset.

Scheduled execution of question lambdas allows you to create cron schedules that may routinely execute question lambdas and optionally put up the outcomes of these queries to webhooks. These webhooks may be hosted externally to Rockset (to additional automate your workflow, for instance to put in writing information 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 lets you have devoted, remoted compute assets (digital situations) per use case. This implies you’ll be able to independently scale and dimension your ingestion VI and a number of secondary VIs which might be used for querying information. Rockset is the primary real-time analytics database to supply this characteristic.

With the mixture of those options, you’ll be able to automate every part you want (besides possibly 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

Usually occasions, there are necessities to ship automated alerts all through the day with outcomes of SQL queries. These may be both enterprise associated (like widespread KPIs that the enterprise is all in favour of) 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 in opposition to Rockset and put up the outcomes of that question to an exterior endpoint comparable to an e-mail supplier or Slack.

Let’s have a look at an e-commerce instance. We now have a set referred to as ShopEvents with uncooked real-time occasions from a webshop. Right here we observe each click on to each product in our webshop, after which ingest this information into Rockset by way of Confluent Cloud. We’re all in favour of realizing what number of objects have been offered on our webshop right now and we need to ship this information 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 received’t undergo the steps of establishing SendGrid, you’ll be able to comply with that in their documentation.

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


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

{{QUERY_ID}} and {{QUERY_RESULTS}} are template values that Rockset gives routinely for scheduled question lambdas so that you could 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 possibly can do the identical with Slack API or some other supplier that accepts POST requests and Authorization headers and also you’ve received your automated alerts arrange!

Should you’re all in favour of sending alerts for gradual queries, have a look at establishing Question Logs the place you’ll be able to see a listing of historic queries and their efficiency.

Use case 2: Creating materialized views or improvement datasets

Rockset helps computerized real-time rollups on ingestion for some information sources. Nonetheless, when you have a have to create further materialized views with extra complicated logic or if you might want to have a replica of your information for different functions (like archival, improvement of latest options, and so forth.), you are able to do it periodically through the use of an INSERT INTO scheduled question lambda. INSERT INTO is a pleasant option to insert the outcomes of a SQL question into an present assortment (it might be the identical assortment or a totally totally different one).

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


Nonetheless, for gross sales analytics functions, we need to make a copy of particular occasions, the place the occasion was a product order. For this, we’ll create a brand new assortment referred to as OrdersAnalytics with none information retention coverage. We’ll then periodically insert information 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 can get all Checkout occasions for the day prior to this:

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 may be certain that we don’t get any duplicates in our orders assortment. Try how Rockset routinely 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 have to 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 information to S3

You need to use scheduled question lambdas to periodically execute a SQL question and export the outcomes of that question to a vacation spot of your selection, comparable to an S3 bucket. That is helpful for eventualities the place you might want to export information frequently, comparable to backing up information, creating studies or feeding information into downstream techniques.

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 prior to this, be a part of that with product metadata and we’ll save this question as a question lambda. That is the dataset we need to periodically export to S3.

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

Subsequent, we’ll have to create an S3 bucket and arrange AWS API Gateway with an IAM Function and Coverage in order that the API gateway can write information to S3. On this weblog, we’ll concentrate on the API gateway half – you’ll want to examine the AWS documentation on how you can create an S3 bucket and the IAM position and coverage.

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

  1. Create a REST API utility 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 technique utilizing the settings under – this primarily permits our endpoint to speak with an S3 bucket referred to as rockset_export:


  • AWS Area: Area in your S3 bucket
  • AWS Service: Easy Storage Service (S3)
  • HTTP technique: PUT
  • Motion Kind: Use path override
  • Path override (non-obligatory): rockset_export/{question _id} (change together with your bucket identify)
  • Execution position: arn:awsiam::###:position/rockset_export (change together with your ARN position)
  • Setup URL Path Parameters and Mapping Templates for the Integration Request – this may extract a parameter referred to as query_id from the physique of the incoming request (we’ll use this as a reputation for recordsdata 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 finished, 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’ve an automatic process that runs each morning at 2 AM, grabs a snapshot of our information 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 assist for auto-scaling digital situations, but when your workload has predictable or nicely understood utilization patterns, you’ll be able to profit from scaling your compute assets up or down based mostly on a set schedule.

That means, you’ll be able to 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 need to use the system).

An instance might 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’ll be able to create a scheduled question lambda that can name Rockset’s digital occasion endpoint and scale it up and down based mostly on a cron schedule.


Comply with these steps:

  1. Create a question lambda with only a choose 1 question, since we don’t really want any particular information for this to work.
  2. Create a schedule for this question lambda. In our case, we need 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 daily indefinitely.
  3. We’ll name the replace digital occasion webhook for the particular VI that we need 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: Organising information analyst environments

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

On this state of affairs, let’s assume we’ve information analysts or information scientists who need to run advert hoc SQL queries to discover information and work on numerous information fashions as a part of a brand new characteristic 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 information analysts, be certain that they will’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 can resume the digital occasion each morning in order that information analysts have an atmosphere prepared after 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 just 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 want any particular information 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 would like 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’ve a working atmosphere for our information analysts and information scientists that’s up and working 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 affords a set of helpful options to automate widespread duties in constructing and sustaining information options. The wealthy set of APIs mixed with the ability of question lambdas and scheduling can help you implement and automate workflows which might be fully hosted and working in Rockset so that you just don’t must depend on third occasion parts or arrange infrastructure to automate repeating duties.

We hope this weblog gave you a number of concepts on how you can do automation in Rockset. Give this a try to tell us the way it works!



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