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

5 Duties to Automate Utilizing Scheduled Question Lambdas in Rockset

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5 Duties to Automate Utilizing Scheduled Question Lambdas in Rockset

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Why and what to automate

As software builders and designers, each time we see repeating duties, we instantly take into consideration methods to automate them. This simplifies our each day work and permits us to be extra environment friendly and targeted on delivering worth to the enterprise.


scheduled-query-lambda-meme

Typical examples of repeating duties embrace scaling compute sources to optimize their utilization from a value 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 growth 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 frequent duties in constructing and managing knowledge options:

  • a wealthy set of APIs so that each side of the platform may be managed via REST
  • Question Lambdas – that are REST API wrappers round your parametrized SQL queries, hosted on Rockset
  • scheduling of Question Lambdas – a lately launched characteristic the place you may create schedules for computerized execution of your question lambdas and submit outcomes of these queries to webhooks
  • compute-compute separation (together with a shared storage layer) which permits isolation and unbiased scaling of compute sources

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

Rockset APIs permit you to work together with all your sources – from creating integrations and collections, to creating digital cases, resizing, pausing and resuming them, to working question lambdas and plain SQL queries.

Question Lambdas provide a pleasant and simple to make use of method to decouple shoppers of information from the underlying SQL queries with the intention to maintain your enterprise 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 mechanically execute question lambdas and optionally submit 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 knowledge again to a supply system or ship an e-mail), however you too can name Rockset APIs and carry out duties like digital occasion resizing and even creating or resuming a digital occasion.

Compute-compute separation permits you to have devoted, remoted compute sources (digital cases) per use case. This implies you may independently scale and measurement 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 characteristic.

With the mix of those options, you may automate the whole lot 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’ll implement them in Rockset.

Use case 1: Sending automated alerts

Usually instances, there are necessities to ship automated alerts all through the day with outcomes of SQL queries. These may be both enterprise associated (like frequent KPIs that the enterprise is fascinated 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 in opposition to Rockset and submit 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’ve a group referred to 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 fascinated about understanding what number of objects have been offered 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.


scheduled-query-lambda-use-case-1

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

SELECT
    COUNT(*) As ItemsSold
FROM
    "Demo-Ecommerce".ShopEvents
WHERE 
    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 organising SendGrid, you may observe that in their documentation.

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


scheduled-query-lambda-use-case-1a

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

{{QUERY_ID}} and {{QUERY_RESULTS}} are template values that Rockset offers mechanically 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:


scheduled-query-lambda-use-case-1b

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

In the event you’re fascinated about sending alerts for sluggish queries, have a look at organising Question Logs the place you may see an inventory of historic queries and their efficiency.

Use case 2: Creating materialized views or growth datasets

Rockset helps computerized real-time rollups on ingestion for some knowledge sources. Nonetheless, when you have a have to create further materialized views with extra advanced logic or if it’s essential to have a replica of your knowledge for different functions (like archival, growth of latest options, and so on.), you are able to do it periodically through the use of an INSERT INTO scheduled question lambda. INSERT INTO is a pleasant method to insert the outcomes of a SQL question into an present assortment (it may very well be the identical assortment or a totally completely different one).

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


scheduled-query-lambda-use-case-2a

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 referred to as OrdersAnalytics with none knowledge retention coverage. We’ll then periodically insert knowledge into this assortment from the uncooked occasions assortment earlier than the information will get purged.


scheduled-query-lambda-use-case-2

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

INSERT INTO "Demo-Ecommerce".OrdersAnalytics
SELECT
    e.EventId AS _id,
    e.Timestamp, 
    e.EventType, 
    e.EventDetails, 
    e.GeoLocation, 
FROM
    "Demo-Ecommerce".ShopEvents e
WHERE 
    e.Timestamp BETWEEN CURRENT_DATE() - DAYS(1) AND CURRENT_DATE()
    AND e.EventType="Checkout";

Word the _id subject we’re utilizing on this question – this can be sure that we don’t get any duplicates in our orders assortment. Take a look at how Rockset mechanically 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.


scheduled-query-lambda-use-case-2b

That’s it – now we’ll have each 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, corresponding to an S3 bucket. That is helpful for eventualities the place it’s essential to export knowledge frequently, corresponding to backing up knowledge, creating reviews 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.


scheduled-query-lambda-use-case-3

Much like our earlier instance, we’ll write a SQL question to get all occasions from yesterday, be 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.

SELECT
    e.Timestamp, 
    e.EventType, 
    e.EventDetails, 
    e.GeoLocation, 
    p.ProductName, 
    p.ProductCategory, 
    p.ProductDescription, 
    p.Value
FROM
    "Demo-Ecommerce".ShopEvents e
    INNER JOIN "Demo-Ecommerce".Merchandise p ON e.EventDetails.ProductID = p._id
WHERE 
    e.Timestamp BETWEEN CURRENT_DATE() - DAYS(1) AND CURRENT_DATE();

Subsequent, we’ll have to create an S3 bucket and arrange AWS API Gateway with an IAM Position and Coverage in order that the API gateway can write knowledge to S3. On this weblog, we’ll concentrate on the API gateway half – you’ll want to examine the AWS documentation on methods to 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 software within the AWS API Gateway service, we are able to name it rockset_export:


scheduled-query-lambda-use-case-3a

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


scheduled-query-lambda-use-case-3b

  1. Create a brand new POST technique utilizing the settings beneath – this basically permits our endpoint to speak with an S3 bucket referred to as rockset_export:


scheduled-query-lambda-use-case-3c

  • AWS Area: Area to your S3 bucket
  • AWS Service: Easy Storage Service (S3)
  • HTTP technique: PUT
  • Motion Sort: Use path override
  • Path override (non-obligatory): rockset_export/{question _id} (change along with your bucket title)
  • Execution position: arn:awsiam::###:position/rockset_export (change along with your ARN position)
  • Setup URL Path Parameters and Mapping Templates for the Integration Request – this can extract a parameter referred to 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):


scheduled-query-lambda-use-case-3d

As soon as that’s carried out, 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:


scheduled-query-lambda-use-case-3e

We’re utilizing {{QUERY_ID}} and {{QUERY_RESULTS}} within the payload configuration and passing them to the API Gateway which is able to use them when exporting to S3 because the title 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, now we have an automatic activity 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 cases

Rockset has assist for auto-scaling digital cases, but when your workload has predictable or effectively understood utilization patterns, you may profit from scaling your compute sources up or down primarily based on a set schedule.

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

An instance may very well be a B2B use case the place your prospects 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 sources throughout these instances.

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


scheduled-query-lambda-use-case-4

Comply with these steps:

  1. Create a question lambda with only a choose 1 question, since we don’t really want 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 each 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.


scheduled-query-lambda-use-case-4a

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 measurement 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 to your advert hoc knowledge evaluation. Every use case can have its personal digital occasion, guaranteeing {that a} manufacturing workload stays secure and performant, with one of the best price-performance for that workload.

On this situation, let’s assume now we 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 characteristic the enterprise desires to roll out. They want entry to collections they usually want compute sources however we don’t need them to create or scale these sources on their very own.

To cater to this requirement, we are able to create a brand new digital occasion devoted to knowledge analysts, be sure 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 might even couple this with use case 2 and create a each day snapshot of manufacturing right into a separate assortment and have the analysts work on that dataset from their digital occasion.


scheduled-query-lambda-use-case-5

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 want any particular knowledge for this to work.
  2. Create a schedule for this question lambda, let’s say each 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.


scheduled-query-lambda-use-case-5a

That’s it! Now now we have a working setting for our knowledge analysts and knowledge 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 is able to droop the VIs at a set schedule.

As demonstrated above, Rockset presents a set of helpful options to automate frequent duties in constructing and sustaining knowledge options. The wealthy set of APIs mixed with the ability of question lambdas and scheduling permit you to implement and automate workflows which might be fully hosted and working in Rockset so that you just don’t should depend on third occasion parts or arrange infrastructure to automate repeating duties.

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



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