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HomeTechnologyThe Common Solvent for REST APIs – O’Reilly

The Common Solvent for REST APIs – O’Reilly


Knowledge scientists working in Python or R usually purchase information by means of REST APIs. Each environments present libraries that allow you to make HTTP calls to REST endpoints, then rework JSON responses into dataframes. However that’s by no means so simple as we’d like. While you’re studying a variety of information from a REST API, you have to 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 approach of studying from APIs that abstracted all of the low-level grunt work and labored the identical approach in all places? Excellent news! That’s precisely what Steampipe does. It’s a device that interprets REST API calls straight into SQL tables. Listed below are three examples of questions that 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 information 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 Massive 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 must 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 kind of columns, question, is particular: it encapsulates Twitter’s question syntax. Right here, we’re simply searching for tweets that match PySpark however we may as simply refine the question by pinning it to particular customers, URLs, sorts (is:retweetis:reply), properties (has:mentionshas_media), and many others. That question syntax is identical irrespective of the way you’re accessing the API: from Python, from R, or from Steampipe. It’s loads to consider, and all it is best to really want to know when crafting queries to mine Twitter information.

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 circumstances the Steampipe documentation not solely reveals you the schemas that govern the mapped tables, it additionally provides examples (TwitterGitHub) 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 homeowners of PySpark-related repositories tweeted currently?

To reply this query we have to seek the advice of two completely different APIs, then be a part of their outcomes. That’s even tougher to do, in a constant approach, while you’re reasoning over REST payloads in Python or R. However that is the type 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 homeowners
github_users as (
  choose
    u.login,
    u.twitter_username
  from
    github_user u
  be a part of
    github_repos r
  on
    r.owner_login = u.login
  the place
    u.twitter_username isn't null
),

-- discover corresponding twitter customers
  choose
    id
  from
    twitter_user t
  be a 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 a 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 5

Right here is the reply:

+----------------+---------------------------------------------------------------+------------------------------------->
| twitter_user   | url                                                           | textual content                                >
+----------------+---------------------------------------------------------------+------------------------------------->
| idealoTech     | https://twitter.com/idealoTech/standing/1524688985649516544     | Can you discover inventive soluti>
|                |                                                               |                                     >
|                |                                                               | Be a part of our @codility Order #API Challe>
|                |                                                               |                                     >
|                |                                                               | #idealolife #codility #php          >
| idealoTech     | https://twitter.com/idealoTech/standing/1526127469706854403     | Our #ProductDiscovery workforce at idealo>
|                |                                                               |                                     >
|                |                                                               | Suppose you may 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 examine the releas>
|                |                                                               |                                     >
|                |                                                               | pip set up -U…                     >
| andfanilo      | https://twitter.com/andfanilo/standing/1524999923665711104      | @edelynn_belle Thanks! Generally it >
| andfanilo      | https://twitter.com/andfanilo/standing/1523676489081712640      | @juliafmorgado Good luck on the reco>
|                |                                                               |                                     >
|                |                                                               | My recommendation: energy via it + a lifeless>
|                |                                                               |                                     >
|                |                                                               | I hated my first few quick movies bu>
|                |                                                               |                                     >
|                |                                                               | Wanting ahead to the video 🙂

When APIs frictionlessly turn into tables, you may dedicate your full consideration to reasoning over the abstractions represented by these APIs. Larry Wall, the creator of Perl, famously mentioned: “Straightforward issues ought to be straightforward, laborious issues ought to be attainable.” The primary two examples are issues that ought to be, and are, straightforward: every is simply 10 traces of easy, straight-ahead SQL that requires no wizardry in any respect.

The third instance is a tougher factor. It will be laborious in any programming language. However SQL makes it attainable in a number of good methods. The answer is made from concise stanzas (CTEs, Widespread Desk Expressions) that kind a pipeline. Every section of the pipeline handles one clearly-defined piece of the issue. You’ll be able to validate the output of every section 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 knowledge.

Do I’ve to make use of SQL?

No! Should you like the concept of mapping APIs to tables, however you’d reasonably motive over these tables in Python or R dataframes, then Steampipe can oblige. Below the covers it’s Postgres, enhanced with international information wrappers that deal with the API-to-table transformation. Something that may hook up with Postgres can hook up 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 eat APIs into dataframes, then motive over the information in Python or R.

However in case you haven’t used SQL on this approach earlier than, it’s price a glance. Contemplate this comparability of SQL to Pandas from Find out how to 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 sort = ‘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 sort = ‘large_airport’ airports[(airports.iso_region == ‘US-CA’) & (airports.type == ‘large_airport’)][[‘ident’, ‘name’, ‘municipality’]]

We are able to argue the deserves of 1 model versus the opposite, however there’s no query that SQL is essentially the most common and widely-implemented option to specific these operations on information. So no, you don’t have to make use of SQL to its fullest potential with a view to profit from Steampipe. However you would possibly discover that you simply wish to.



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