Why and what to automate
As software builders and designers, each time we see repeating duties, we instantly take into consideration learn how to automate them. This simplifies our every day work and permits us to be extra environment friendly and targeted on delivering worth to the enterprise.
Typical examples of repeating duties embody 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 information to S3 buckets, and so forth.
How Rockset helps with automation
Rockset provides a set of highly effective options to assist automate frequent duties in constructing and managing information options:
- a wealthy set of APIs so that each side of the platform will be managed by way 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’ll be able to create schedules for automated 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 assist 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 supply a pleasant and straightforward to make use of method to decouple customers of information from the underlying SQL queries in an effort to preserve 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 routinely execute question lambdas and optionally submit the outcomes of these queries to webhooks. These webhooks will be hosted externally to Rockset (to additional automate your workflow, for instance to jot down information again to a supply system or ship an e-mail), however it’s also possible to 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’ll be able to independently scale and measurement your ingestion VI and a number of secondary VIs which can be used for querying information. Rockset is the primary real-time analytics database to supply this function.
With the mix of those options, you’ll be able to automate all the pieces you want (besides perhaps brewing your espresso)!
Typical use instances for automation
Let’s now have a look into typical use instances for automation and present how you’ll implement them in Rockset.
Use case 1: Sending automated alerts
Typically instances, there are necessities to ship automated alerts all through the day with outcomes of SQL queries. These will be both enterprise associated (like frequent KPIs that the enterprise is serious 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 resembling an e-mail supplier or Slack.
Let’s have a look at an e-commerce instance. We have now a set 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 information into Rockset through Confluent Cloud. We’re serious about figuring out what number of objects had been bought on our webshop right now and we need to ship this information through e-mail to our enterprise customers each six hours.
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 gained’t undergo the steps of establishing SendGrid, you’ll be able to comply with that in their documentation.
When you’ve acquired an API key from SendGrid, you’ll be able to create a schedule on 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 bought objects that day.
{{QUERY_ID}}
and {{QUERY_RESULTS}}
are template values that Rockset supplies routinely for scheduled question lambdas in an effort 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 possibly can do the identical with Slack API or some other supplier that accepts POST requests and Authorization
headers and also you’ve acquired your automated alerts arrange!
When you’re serious about sending alerts for sluggish 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 growth datasets
Rockset helps automated real-time rollups on ingestion for some information sources. Nevertheless, in case you have a have to create extra materialized views with extra advanced logic or if you must have a duplicate of your information 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 current 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 have now a knowledge retention coverage set on our ShopEvents
assortment in order that occasions which can be older than 12 months routinely get faraway from Rockset.
Nevertheless, 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 are able to do that by making a SQL question that can get all Checkout
occasions for the day past:
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 – it will be certain that we don’t get any duplicates in our orders assortment. Take a look at 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 every day product orders saved in our OrdersAnalytics
assortment, prepared to be used.
Use case 3: Periodic exporting of information 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, resembling an S3 bucket. That is helpful for eventualities the place you must export information frequently, resembling backing up information, creating experiences or feeding information into downstream programs.
On this instance, we are going to 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.
Just like our earlier instance, we’ll write a SQL question to get all occasions from the day past, be 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.
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 Function and Coverage in order that the API gateway can write information to S3. On this weblog, we’ll deal with the API gateway half – make sure to examine the AWS documentation on learn how to create an S3 bucket and the IAM function and coverage.
Comply with these steps to arrange AWS API Gateway so it’s prepared to speak with our scheduled question lambda:
- Create a REST API software within the AWS API Gateway service, we are able to name it
rockset_export
:
- Create a brand new useful resource which our question lambdas will use, we’ll name it
webhook
:
- 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 on your S3 bucket
- AWS Service:
Easy Storage Service (S3)
- HTTP technique:
PUT
- Motion Sort:
Use path override
- Path override (optionally available):
rockset_export/{question _id}
(change along with your bucket title) - Execution function:
arn:awsiam::###:function/rockset_export
(change along with your ARN function) - Setup URL Path Parameters and Mapping Templates for the Integration Request – it will 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) andquery_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 are able to 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 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 information 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 help for auto-scaling digital cases, but when your workload has predictable or effectively understood utilization patterns, you’ll be able to profit from scaling your compute sources up or down primarily based on a set schedule.
That approach, you’ll be able to 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 need to use the system).
An instance might 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’ll be able to create a scheduled question lambda that can name Rockset’s digital occasion endpoint and scale it up and down primarily based on a cron schedule.
Comply with these steps:
- Create a question lambda with only a
choose 1
question, since we don’t really want any particular information for this to work. - 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 are going to set limitless variety of executions in order that it runs every single day indefinitely. - We’ll name the replace digital occasion webhook for the precise 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 likeMEDIUM
orLARGE
within the physique of the request.
We are able to 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 information analyst environments
With Rockset’s compute-compute separation, it’s simple to spin up devoted, remoted and scalable environments on 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 now we have 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 function 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 information analysts, be certain that they’ll’t edit or create VIs by making a customized RBAC function and assign analysts to that function, 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 setting prepared once they log into the Rockset console. We might even couple this with use case 2 and create a every 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:
- Create a question lambda with only a
choose 1
question, since we don’t really want any particular information for this to work. - Create a schedule for this question lambda, let’s say every day at 8 AM Monday to Friday and we are going to 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
. - 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 now we have a working setting for our information analysts and information scientists that’s up and working for them each work day at 8 AM. We are able to 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 provides a set of helpful options to automate frequent duties in constructing and sustaining information options. The wealthy set of APIs mixed with the facility of question lambdas and scheduling assist you to implement and automate workflows which can be utterly hosted and working in Rockset so that you just don’t should depend on third celebration parts or arrange infrastructure to automate repeating duties.
We hope this weblog gave you just a few concepts on learn how to do automation in Rockset. Give this a try to tell us the way it works!