Build a Low Latency, Globally Distributed Python App using Macrometa's Geo-Distributed Database Cloud.

Celý článek

In this tutorial, I'm going to show you how to quickly and easily build a distributed database backend that spans across North America (Los Angeles & Ashburn VA), Europe (Frankfurt) and Asia (Mumbai, India).

We're going to build simple python application on Macrometa's serverless geo-distributed database cloud (https://www.macrometa.co) to create a data serving backend that will span across multiple worldwide data centers.

This is so that when end users run your app, it doesn't need to fetch data from a database that is thousands of miles and hundreds of milliseconds away but instead gets its data from the closest running database in a region closest to it.

So if you're wondering what Macrometa is? Macrometa is a geo-distributed database cloud service and available for free for developers to build apps on. Macrometa provides the following capabilities:

1. A Serverless real-time NoSQL geo-distributed database that serves data as Key/Value pairs, JSON Documents, Streams, Graphs and time series. The geo-distributed bit is important here because that what makes Macrometa special - it can very efficiently, quickly and accurately replicate and synchronize your data across 100s of worldwide locations while hiding the complexities in providing correct concurrent behavior.
2. A fully integrated Stream engine to let you query and manipulate data in motion without needing to wait for it to be ingested into the database (I will do a separate tutorial on this in a few days).
3. A serverless compute runtime that provides a Web Assembly in V8 engine for you to run your code with hyper-locality to your data (this will be coming out in the second half of 2019 and is not in the currently available product)/

Learn more at https://www.macrometa.co

So with that background - let's dive right in.

There are 2 ways to do this tutorial:

1. For those who want to code - the repl.it below is for you. You can see how to build a simple application to call Macrometa's APIs in Python to do interesting things.
2. For those of you who prefer a web console or GUI - go to https://www.macrometa.co/quickstart

Building an application consists of 3 basic steps:

1. Create a geo fabric or use the global fabric.
2. Create a collection for your data (a collection is like a table in SQL).
3. Send queries to CReate, Update, Delete (CRUD) documents (rows) in your collection.

from c8 import C8Client
import json

print("Connecting to Macrometa global Fabric ...")
client = C8Client(protocol='https', host='try.macrometa.io', port=443)
#Add your tenant details, user id and password below
fabric = client.fabric(
    tenant='OutKast630',
    name='_system',
    username='root',
    password='KleverP@$$word')
print("\nConnected to Macrometa global Fabric ...")

print("\nGetting geo fabric details...")
print(json.dumps(fabric.fabrics_detail(), indent=4))

# creating the address book collection & add some contacts if it doesnt exist
if not fabric.has_collection('addresses'):
    addresses = fabric.create_collection('addresses')
    print("\n\nCreated addresses collection...")

else:
    print("\n\naddresses collection exists")
    addresses = fabric.collection('addresses')

    print("\n\n Inserting contacts into addresses")
cursor = fabric.c8ql.execute(
    'FOR persons IN [ { firstname: "Joseph", lastname: "Smith", email: "jsmith2020@gmail.com" },{ firstname: "Astrid", lastname: "Young", email: "missmoneybags@young.co.sg" },{ firstname: "Boris", lastname: "Balastikov", email: "bb@refundit.com" },{ firstname: "Sherlock", lastname: "Jones", email: "pd@elementary.org" },{ firstname: "Alpha", lastname: "Simpson", email: "alf@simpsonrealtech.com" },{ firstname: "Jose", lastname: "Garcia", email: "j.garcia@nebulus.com" },{ firstname: "Lee", lastname: "Ki", email: "Lee.ki@symbol.com" },{ firstname: "Mark", lastname: "Goldfine", email: "mark@tidalwave.com" },{ firstname: "Ramesh", lastname: "Sriram", email: "ramesh@lifely.com" }] INSERT persons INTO addresses'
)
docs = [document for document in cursor]
print("\n\nInserted some contact data...")

#Run a query using the built in query language C8QL
print("\n\nExecuting C8QL to retrieve all contacts...")
cursor = fabric.c8ql.execute('FOR contact IN addresses RETURN contact')
docs = [document for document in cursor]
print(json.dumps((docs), indent=4))

#Truncate the collection so that we leave it all cleaned up for the next person
print("\n\nTruncating collection to zero docs ...")
addresses.truncate()
cursor = fabric.c8ql.execute('FOR contact IN addresses RETURN contact')
docs = [document for document in cursor]
print(json.dumps((docs), indent=4))
print("\nCollection truncated ...")

print("And we're done!")