Knowledge scientists working in Python or R usually purchase knowledge by means of REST APIs. Each environments present libraries that show you how to make HTTP calls to REST endpoints, then remodel JSON responses into dataframes. However that’s by no means so simple as we’d like. If you’re studying quite a lot of knowledge from a REST API, you could do it a web page at a time, however pagination works in a different way from one API to the subsequent. So does unpacking the ensuing JSON buildings. HTTP and JSON are low-level requirements, and REST is a loosely-defined framework, however nothing ensures absolute simplicity, by no means thoughts consistency throughout APIs.
What if there have been a method of studying from APIs that abstracted all of the low-level grunt work and labored the identical method in all places? Excellent news! That’s precisely what Steampipe does. It’s a device that interprets REST API calls instantly into SQL tables. Listed here are three examples of questions which you could ask and reply utilizing Steampipe.
Study quicker. Dig deeper. See farther.
1. Twitter: What are latest tweets that point out PySpark?
Right here’s a SQL question to ask that query:
choose
id,
textual content
from
twitter_search_recent
the place
question = 'pyspark'
order by
created_at desc
restrict 5;Right here’s the reply:
+---------------------+------------------------------------------------------------------------------------------------>
| id | textual content >
+---------------------+------------------------------------------------------------------------------------------------>
| 1526351943249154050 | @dump Tenho trabalhando bastante com Spark, mas especificamente o PySpark. Vale a pena usar um >
| 1526336147856687105 | RT @MitchellvRijkom: PySpark Tip ⚡ >
| | >
| | When to make use of what StorageLevel for Cache / Persist? >
| | >
| | StorageLevel decides how and the place knowledge ought to be s… >
| 1526322757880848385 | Remedy challenges and exceed expectations with a profession as a AWS Pyspark Engineer. https://t.co/>
| 1526318637485010944 | RT @JosMiguelMoya1: #pyspark #spark #BigData curso completo de Python y Spark con PySpark >
| | >
| | https://t.co/qf0gIvNmyx >
| 1526318107228524545 | RT @money_personal: PySpark & AWS: Grasp Large Knowledge With PySpark and AWS >
| | #ApacheSpark #AWSDatabases #BigData #PySpark #100DaysofCode >
| | -> http… >
+---------------------+------------------------------------------------------------------------------------------------>The desk that’s being queried right here, twitter_search_recent, receives the output from Twitter’s /2/tweets/search/latest endpoint and formulates it as a desk with these columns. You don’t should make an HTTP name to that API endpoint or unpack the outcomes, you simply write a SQL question that refers back to the documented columns. A type of columns, question, is particular: it encapsulates Twitter’s question syntax. Right here, we’re simply on the lookout for tweets that match PySpark however we may as simply refine the question by pinning it to particular customers, URLs, sorts (is:retweet, is:reply), properties (has:mentions, has_media), and so forth. That question syntax is identical irrespective of the way you’re accessing the API: from Python, from R, or from Steampipe. It’s lots to consider, and all you need to actually need to know when crafting queries to mine Twitter knowledge.
2. GitHub: What are repositories that point out PySpark?
Right here’s a SQL question to ask that query:
choose
title,
owner_login,
stargazers_count
from
github_search_repository
the place
question = 'pyspark'
order by stargazers_count desc
restrict 10;Right here’s the reply:
+----------------------+-------------------+------------------+
| title | owner_login | stargazers_count |
+----------------------+-------------------+------------------+
| SynapseML | microsoft | 3297 |
| spark-nlp | JohnSnowLabs | 2725 |
| incubator-linkis | apache | 2524 |
| ibis | ibis-project | 1805 |
| spark-py-notebooks | jadianes | 1455 |
| petastorm | uber | 1423 |
| awesome-spark | awesome-spark | 1314 |
| sparkit-learn | lensacom | 1124 |
| sparkmagic | jupyter-incubator | 1121 |
| data-algorithms-book | mahmoudparsian | 1001 |
+----------------------+-------------------+------------------+This seems similar to the primary instance! On this case, the desk that’s being queried, github_search_repository, receives the output from GitHub’s /search/repositories endpoint and formulates it as a desk with these columns.
In each instances the Steampipe documentation not solely exhibits you the schemas that govern the mapped tables, it additionally offers examples (Twitter, GitHub) of SQL queries that use the tables in varied methods.
Be aware that these are simply two of many obtainable tables. The Twitter API is mapped to 7 tables, and the GitHub API is mapped to 41 tables.
3. Twitter + GitHub: What have house owners of PySpark-related repositories tweeted recently?
To reply this query we have to seek the advice of two completely different APIs, then be part of their outcomes. That’s even tougher to do, in a constant method, while you’re reasoning over REST payloads in Python or R. However that is the form of factor SQL was born to do. Right here’s one option to ask the query in SQL.
-- discover pyspark repos
with github_repos as (
choose
title,
owner_login,
stargazers_count
from
github_search_repository
the place
question = 'pyspark' and title ~ 'pyspark'
order by stargazers_count desc
restrict 50
),
-- discover twitter handles of repo house owners
github_users as (
choose
u.login,
u.twitter_username
from
github_user u
be part of
github_repos r
on
r.owner_login = u.login
the place
u.twitter_username will not be null
),
-- discover corresponding twitter customers
choose
id
from
twitter_user t
be part of
github_users g
on
t.username = g.twitter_username
)
-- discover tweets from these customers
choose
t.author->>'username' as twitter_user,
'https://twitter.com/' || (t.author->>'username') || '/standing/' || t.id as url,
t.textual content
from
twitter_user_tweet t
be part of
twitter_userids u
on
t.user_id = u.id
the place
t.created_at > now()::date - interval '1 week'
order by
t.creator
restrict 5Right here is the reply:
+----------------+---------------------------------------------------------------+------------------------------------->
| twitter_user | url | textual content >
+----------------+---------------------------------------------------------------+------------------------------------->
| idealoTech | https://twitter.com/idealoTech/standing/1524688985649516544 | Can you discover artistic soluti>
| | | >
| | | Be part of our @codility Order #API Challe>
| | | >
| | | #idealolife #codility #php >
| idealoTech | https://twitter.com/idealoTech/standing/1526127469706854403 | Our #ProductDiscovery staff at idealo>
| | | >
| | | Suppose you possibly can resolve it? 😎 >
| | | ➡️ https://t.co/ELfUfp94vB https://t>
| ioannides_alex | https://twitter.com/ioannides_alex/standing/1525049398811574272 | RT @scikit_learn: scikit-learn 1.1 i>
| | | What's new? You'll be able to test the releas>
| | | >
| | | pip set up -U… >
| andfanilo | https://twitter.com/andfanilo/standing/1524999923665711104 | @edelynn_belle Thanks! Typically it >
| andfanilo | https://twitter.com/andfanilo/standing/1523676489081712640 | @juliafmorgado Good luck on the reco>
| | | >
| | | My recommendation: energy by it + a useless>
| | | >
| | | I hated my first few brief movies bu>
| | | >
| | | Trying ahead to the video 🙂When APIs frictionlessly change into tables, you possibly can dedicate your full consideration to reasoning over the abstractions represented by these APIs. Larry Wall, the creator of Perl, famously stated: “Simple issues ought to be straightforward, laborious issues ought to be doable.” The primary two examples are issues that ought to be, and are, straightforward: every is simply 10 strains of straightforward, straight-ahead SQL that requires no wizardry in any respect.
The third instance is a tougher factor. It could be laborious in any programming language. However SQL makes it doable in a number of good methods. The answer is made from concise stanzas (CTEs, Widespread Desk Expressions) that kind a pipeline. Every part of the pipeline handles one clearly-defined piece of the issue. You’ll be able to validate the output of every part earlier than continuing to the subsequent. And you are able to do all this with essentially the most mature and widely-used grammar for choice, filtering, and recombination of information.
Do I’ve to make use of SQL?
No! If you happen to like the concept of mapping APIs to tables, however you’ll fairly cause over these tables in Python or R dataframes, then Steampipe can oblige. Below the covers it’s Postgres, enhanced with overseas knowledge wrappers that deal with the API-to-table transformation. Something that may connect with Postgres can connect with Steampipe, together with SQL drivers like Python’s psycopg2 and R’s RPostgres in addition to business-intelligence instruments like Metabase, Tableau, and PowerBI. So you need to use Steampipe to frictionlessly devour APIs into dataframes, then cause over the information in Python or R.
However should you haven’t used SQL on this method earlier than, it’s price a glance. Contemplate this comparability of SQL to Pandas from rewrite your SQL queries in Pandas.
| SQL | Pandas |
|---|---|
| choose * from airports | airports |
| choose * from airports restrict 3 | airports.head(3) |
| choose id from airports the place ident = ‘KLAX’ | airports[airports.ident == ‘KLAX’].id |
| choose distinct kind from airport | airports.kind.distinctive() |
| choose * from airports the place iso_region = ‘US-CA’ and kind = ‘seaplane_base’ | airports[(airports.iso_region == ‘US-CA’) & (airports.type == ‘seaplane_base’)] |
| choose ident, title, municipality from airports the place iso_region = ‘US-CA’ and kind = ‘large_airport’ | airports[(airports.iso_region == ‘US-CA’) & (airports.type == ‘large_airport’)][[‘ident’, ‘name’, ‘municipality’]] |
We will argue the deserves of 1 fashion versus the opposite, however there’s no query that SQL is essentially the most common and widely-implemented option to categorical these operations on knowledge. So no, you don’t have to make use of SQL to its fullest potential with the intention to profit from Steampipe. However you would possibly discover that you simply wish to.
