Shift: Real World Migration from MongoDB to Cassandra

Sep 6, 20130 likes4,974 views

Presentation on SHIFT's migration from MongoDB to Cassandra. Topics will include reasons behind choosing to move to Cassandra, zero downtime migration strategy, data modeling patterns, and the benefits of using CQL3.

SHIFT.com
Migrating from MongoDB to Cassandra
by: Blake Eggleston & Jon Haddad

What is SHIFT.com?
Shift is a platform that enables marketers to
communicate across organizations and
departments in one single place.
It’s also an open application platform with a
set of applications built on top of it that can
communicate with one another.

Initial Stack
●  Python
○  Flask
○  Celery
●  MongoDB
○  mongoengine
●  Neo4j / Titan
○  Bulbs
○  thunderdome
●  Redis
●  AWS
○  m1.xlarge for mongo

Current Stack
●  Python
○  still flask
○  still celery
○  gevent (it rocks)
●  Cassandra
○  1.2.6
○  cqlengine
●  ElasticSearch
●  Redis
○  jondis
●  AWS

Why did we move to Cassandra?
●  Operational Benefits
○  Adding and removing nodes is much easier,
compared to Mongo’s shards
●  Control over our Data on Disk (LSMT)
●  Love CQL3
●  Long term scalability
○  Scales Linearly
○  Multi DC Support Baked in

Migration Goals
●  Zero downtime
○  We wanted to roll out Cassandra without any
service interruptions
●  No loss of performance
○  By carefully structuring our schema we were able
to match MongoDB’s performance.

Benefits of CQL3
●  Easy to understand if you’re coming from
RDBMS
●  Collections
○  sets, lists, maps
●  Batch Queries
●  Clustering Keys
○  Handles ordering of logical rows
○  Saved us from column name management scheme
and allowed us to focus on our data

Data Modelling Patterns
●  considerations: working with Mongo’s dbrefs
and optimizing layout on disk
●  structured tables as materialized views of
the queries we planned on using
●  moving multiple documents into a single
physical row
●  creating supporting index tables for looking
up logical rows

Time Series: Message Stream
●  Users have tens of thousands of messages
●  Each users message stream is specific to
them, like a twitter feed
●  This is Cassandra’s strength - Time Series
●  Considered Redis - but poor for multi-dc
create table news_feed (
user_id uuid,
message_id timeuuid,
message,

cqlengine
●  cqlengine.org
●  the Python CQL3 object-row mapper
●  exposes CQL3 tables as Python classes
●  maps columns to properties
●  builds CQL queries
#model definition
class ExampleModel(Model):
example_id = columns.UUID(primary_key=True)
example_type = columns.Integer(index=True)
created_at = columns.DateTime()
description = columns.Text(required=False)
# example query
ExampleModel.objects(example_type=1)

Improvements from moving to C*
●  Operationally we’ve had zero problems
●  Outstanding Performance
●  Easy to build new features
●  Community has been amazing (mailing list
and #cassandra)

misc tips
●  leveled compaction - good for read heavy
workloads
●  use secondary indexes sparingly,
understand how they work and when to use
them
●  to reiterate, think about how you’re going
to query your data
●  use elastic search / solr for ad hoc queries

Contact Info
Jon Haddad
@rustyrazorblade
jon@shift.com
Blake Eggleston
@blakeeggleston
blake@shift.com
….we’re hiring!

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NetflixOSS Meetup season 3 episode 1Ruslan Meshenberg

This summary provides an overview of the key points from the document in 3 sentences: The document outlines the agenda for Season 3 Episode 1 of the Netflix OSS podcast, which includes lightning talks on 8 new projects including Atlas, Prana, Raigad, Genie 2, Inviso, Dynomite, Nicobar, and MSL. Representatives from Netflix, IBM Watson, Nike Digital, and Pivotal then each provide a 3-5 minute presentation on their featured project. The presentations describe the motivation, features and benefits of each project for observability, integration with the Netflix ecosystem, automation of Elasticsearch deployments, job scheduling, dynamic scripting for Java, message security, and developing microservices

Cassandra data accesstechblog

This document discusses Cassandra data access in Java at eBuddy, which uses Cassandra for user data services, user discovery services, persistent session stores, message histories, and location-based discovery. It provides statistics on eBuddy's Cassandra usage and an overview of the design objectives and layered architecture for data access. Key aspects covered include the use of generics, interfaces, and serializers for reading and writing data, as well as data access objects that transform data models to domain models. The document also discusses next steps related to CQL3 and object mapping frameworks.

eBuddy | Cassandra Data Access in JavaDataStax Academy

This document discusses Cassandra data access in Java applications. It provides an overview of how eBuddy uses Cassandra for user data services, user discovery services, persistent session stores, and message histories. It then discusses some statistics on eBuddy's Cassandra usage and sizes. It outlines design objectives for data access and describes the layered approach used, including operations, data access, and domain object layers. Finally, it discusses next steps related to CQL3, object mapping frameworks, and hierarchical property modeling.

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Mwai Karimi gave an introduction to MongoDB, a scalable document-oriented database. Some key points: - MongoDB uses a flexible document data model and scales horizontally with sharding. It supports rich queries and indexing. - Documents correspond to objects in programming languages, reducing need for joins. Embedded documents and dynamic schemas provide flexibility. - CRUD operations allow creating, reading, updating, and deleting documents. Collections contain documents and scale out across servers. - MongoDB supports features like replication, auto-sharding, security controls, and disaster recovery through Ops Manager to provide high availability, scalability, and manageability.

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MongoDBwiTTyMinds1

MongoDB is a cross-platform document-oriented database program that uses JSON-like documents with dynamic schemas, commonly referred to as a NoSQL database. It allows for embedding of documents and arrays within documents, hierarchical relationships between data, and indexing of data for efficient queries. MongoDB is developed by MongoDB Inc. and commonly used for big data and content management applications due to its scalability and ease of horizontal scaling.

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What are the major components of MongoDB and the major tools used in it.docxTechnogeeks

MongoDB, a renowned NoSQL database, comprises key components like databases, collections, documents, indexes, replica sets, and sharding, enabling flexible and scalable data management. Major tools include the Mongo Shell, MongoDB Compass, MongoDB Atlas, and Mongoose, facilitating database administration, monitoring, and development tasks. MongoDB's optimization strategies involve indexing, efficient querying, projection, aggregation, and sharding to enhance query performance. Capped collections offer a specialized solution for managing time-ordered data with predictable sizes, ensuring high performance and simplicity for specific use cases like event logging. Understanding MongoDB's components, utilizing its tools, and implementing optimization strategies empower developers to build modern, scalable, and efficient applications tailored to their needs.

MongodbThiago Veiga

MongoDB is an open-source document database that provides high performance, high availability, and automatic scaling. It stores data in flexible, JSON-like documents, enabling storage of data with complex relationships easily and supporting polyglot persistence. MongoDB can be used for applications such as content management systems, user profiles, logs, and more. It provides indexing, replication, load balancing and aggregation capabilities.

MongoDB 4.0 새로운 기능 소개Ha-Yang(White) Moon

This document provides an agenda and overview for a presentation on MongoDB 4.0. The presentation will cover what's new in MongoDB 4.0 including working with data, building distributed systems, enabling cloud data strategies, and serverless and mobile capabilities. It will also discuss the intelligent operational data platform provided by MongoDB for working with data, putting data where needed intelligently, and running applications anywhere.

Kubernetes for BeginnersDigitalOcean

Kubernetes 101 provides an overview of containers, Kubernetes architecture, and Kubernetes objects. It discusses how containers evolved from virtualization to improve efficiency. Kubernetes is introduced as a container orchestration tool to manage deployments, scaling, networking, security etc. of containers across clusters. Key Kubernetes components like the control plane, nodes, and objects like pods, deployments, services, storage and secrets are explained at a high level.

Running Cassandra in AWSDataStax Academy

For this upcoming meetup, we welcome Patrick Eaton PhD, Systems Architect at Stackdriver, and Joey Imbasciano, Cloud Platform Engineer at Stackdriver. What You'll Learn At This Meetup: • Why Stackdriver chose Cassandra over other DB offerings • Stackdriver's data pipeline that runs into Cassandra • Operating Cassandra Running on AWS • Stackdriver's approach to disaster recovery Patrick and Joey will be presenting their use of Apache Cassandra at Stackdriver, some lesson's learned, technical tips and a Q&A to end the evening.

Armada: Batch Workloads Across 10s of Kubernetes ClustersKubernetesCommunityD

Presented by Kevin Hannon, Open Source Developer, G-Research Open Source, at Kubernetes Community Days, Washington DC, September 14, 2022 ● Background in Chemistry and High Performance Computing ● Entrypoint into Kubernetes was focused on enabling running scientific workflows across high performance computing clusters and/or Kubernetes clusters ● Working at G-Research Open Source focused on enabling batch workloads on multiple kubernetes clusters

Introduction To MongoDBElieHannouch

This document provides an introduction and overview of MongoDB. It begins with defining what a database and NoSQL database are. MongoDB is introduced as a popular open-source document-oriented NoSQL database that stores data in BSON documents. The document outlines some key advantages of MongoDB like its flexibility and support for many programming languages. It then covers how to set up a local MongoDB server, perform basic CRUD operations, and query documents. Finally, it introduces MongoDB Atlas as a cloud database service that handles deploying and managing MongoDB in the cloud.

Building Microservices with Apache Kafka by Colin McCabeData Con LA

Abstract:- Building distributed systems is challenging. Luckily, Apache Kafka provides a powerful toolkit for putting together big services as a set of scalable, decoupled components. In this talk, I'll describe some of the design tradeoffs when building microservices, and how Kafka's powerful abstractions can help. I'll also talk a little bit about what the community has been up to with Kafka Streams, Kafka Connect, and exactly-once semantics.

MongoDB Versatility: Scaling the MapMyFitness PlatformMongoDB

Chris Merz, Manager of Operations, MapMyFitness The MMF user base more than doubled in 2011, beginning an era of rapid data growth. With Big Data come Big Data Headaches. The traditional MySQL solution for our suite of web applications had hit its ceiling. MongoDB was chosen as the candidate for exploration into NoSQL implementations, and now serves as our go-to data store for rapid application deployment. This talk will detail several of the MongoDB use cases at MMF, from serving 2TB+ of geolocation data, to time-series data for live tracking, to user sessions, app logging, and beyond. Topics will include migration patterns, indexing practices, backend storage choices, and application access patterns, monitoring, and more.

Cassandra meetup slides - Oct 15 Santa Monica ColoftJon Haddad

Building RESTtful services in MEANMadhukara Phatak

NetflixOSS Meetup season 3 episode 1Ruslan Meshenberg

Cassandra data accesstechblog

eBuddy | Cassandra Data Access in JavaDataStax Academy

[Virtual Meetup] Using Elasticsearch as a Time-Series Database in the Endpoin...Anna Ossowski

Introduction to mongo dbLawrence Mwai

Big data @ Hootsuite analtyicsClaudiu Coman

MongoDB vs ScyllaDB: Tractian’s Experience with Real-Time MLScyllaDB

MongoDBwiTTyMinds1

Kyryl Truskovskyi: Kubeflow for end2end machine learning lifecycleLviv Startup Club

What are the major components of MongoDB and the major tools used in it.docxTechnogeeks

MongodbThiago Veiga

MongoDB 4.0 새로운 기능 소개Ha-Yang(White) Moon

Kubernetes for BeginnersDigitalOcean

Running Cassandra in AWSDataStax Academy

Armada: Batch Workloads Across 10s of Kubernetes ClustersKubernetesCommunityD

Introduction To MongoDBElieHannouch

Building Microservices with Apache Kafka by Colin McCabeData Con LA

MongoDB Versatility: Scaling the MapMyFitness PlatformMongoDB

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