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HomeTechnologyThe Common Solvent for REST APIs – O’Reilly

The Common Solvent for REST APIs – O’Reilly


Information scientists working in Python or R sometimes purchase information by means of REST APIs. Each environments present libraries that aid you 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 loads of information from a REST API, you might want to do it a web page at a time, however pagination works in another way from one API to the subsequent. So does unpacking the ensuing JSON constructions. 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 manner of studying from APIs that abstracted all of the low-level grunt work and labored the identical manner in every single place? Excellent news! That’s precisely what Steampipe does. It’s a software that interprets REST API calls instantly into SQL tables. Listed here are three examples of questions that you could ask and reply utilizing Steampipe.


Be taught 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 needs to be s…                                           >
| 1526322757880848385 | Clear up 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 Information 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 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 might as simply refine the question by pinning it to particular customers, URLs, sorts (is:retweetis:reply), properties (has:mentionshas_media), and so forth. That question syntax is similar irrespective of the way you’re accessing the API: from Python, from R, or from Steampipe. It’s a lot to consider, and all it’s best to actually need 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 appears 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 exhibits you the schemas that govern the mapped tables, it additionally offers examples (TwitterGitHub) of SQL queries that use the tables in numerous methods.

Word that these are simply two of many out there 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 these days?

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 manner, once 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 strategy 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 shouldn't 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.writer
restrict 5

Right here is the reply:

+----------------+---------------------------------------------------------------+------------------------------------->
| twitter_user   | url                                                           | textual content                                >
+----------------+---------------------------------------------------------------+------------------------------------->
| idealoTech     | https://twitter.com/idealoTech/standing/1524688985649516544     | Can you discover artistic soluti>
|                |                                                               |                                     >
|                |                                                               | Be a part of our @codility Order #API Challe>
|                |                                                               |                                     >
|                |                                                               | #idealolife #codility #php          >
| idealoTech     | https://twitter.com/idealoTech/standing/1526127469706854403     | Our #ProductDiscovery crew at idealo>
|                |                                                               |                                     >
|                |                                                               | Assume 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 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 via it + a useless>
|                |                                                               |                                     >
|                |                                                               | I hated my first few quick movies bu>
|                |                                                               |                                     >
|                |                                                               | Wanting ahead to the video 🙂

When APIs frictionlessly grow to be tables, you may dedicate your full consideration to reasoning over the abstractions represented by these APIs. Larry Wall, the creator of Perl, famously stated: “Straightforward issues needs to be simple, arduous issues needs to be potential.” The primary two examples are issues that needs to be, and are, simple: 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 might be arduous in any programming language. However SQL makes it potential 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 probably the most mature and widely-used grammar for choice, filtering, and recombination of knowledge.

Do I’ve to make use of SQL?

No! If you happen to like the thought of mapping APIs to tables, however you’d quite purpose over these tables in Python or R dataframes, then Steampipe can oblige. Below the covers it’s Postgres, enhanced with overseas 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 should utilize Steampipe to frictionlessly devour APIs into dataframes, then purpose over the info in Python or R.

However in the event you haven’t used SQL on this manner earlier than, it’s value a glance. Contemplate this comparability of SQL to Pandas from How one can rewrite your SQL queries in Pandas.

SQLPandas
choose * from airportsairports
choose * from airports restrict 3airports.head(3)
choose id from airports the place ident = ‘KLAX’airports[airports.ident == ‘KLAX’].id
choose distinct sort from airportairports.sort.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 model versus the opposite, however there’s no query that SQL is probably the most common and widely-implemented strategy to categorical these operations on information. 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 need to.



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