A guided tour of the BigData technologies zoo
#Itamar Syn-Hershko

Big Data is a buzzword. “Big Data is any thing which will crash Excel” (@devopsborat)

Even if you don’t have Big Data, these tools and technologies can still be useful.

Agenda – Data at Rest, Streams, Moving data around

There are a LOT of tools and technologies around big data.

Where are we today:
* Database Schemas
* Unreliable at scale
* Expensive at scale
* Relational mindset
* Data is being moved from storage to compute

Schemas assume structured data, can be hard to set up, and are hard to adapt (lack agility)
Scaling strategy is bigger machines (scaling up) which is more expensive than scaling out (multiple simple machines)

Quote from Grace Hopper (heavily paraphrased):
You can’t grow larger oxen, so you need to get another ox to move a bigger load.

Hadoop – based on Google File System and MapReduce
Commodity hardware
Created by Doug Cutting and Mike Cafarella. Open sourced under Apache.
The original product was called Nutch in 2002. Became Hadoop in 2006.
HDFS – Hadoop Distributed File System
Basically takes a big file and store it on a lot of servers. Divide the file into partitions and store each partition on different servers. Essentially sharding. The partitions are each stored on more than one machine for protection.
There is a NameNode that manages how the data is partitioned and how to reassemble it. Losing NameNode is a problem, so it needs to have redundancy.

More DFS: S3, CephFS, GlusterFS, Lustre

Dedicated File Formats: SequenceFile, RCFile, Avro,

MapReduce – parallel computations on data, based on functional programming concepts
Map processes documents in some way (take sentences and break them in to words, for example), producing tuples.
Reduce takes the tuples and combines them (so take each word and add up the counts)

Hadoop does this in Java – you write a Mapper and a Reducer (they implement interfaces). Then you put the .jar files on Hadoop and it runs the job in place using TaskTrackers. These TaskTrackers are controlled by a JobTracker, which runs the job and spins up the TaskTrackers to do the work. There will be a TaskTracker for each partition of data. This is how parallelism is achieved.

Hadoop now has a bunch of distributions. Apache, Cloudera, Hortonworks are the key ones. Each beyond Apache adds other technologies on top (Impala; HCatalog, Tez). Various features are added by cloud vendors to make managing Hadoop in the cloud easier, too.

Apache Hive – Runs SQL over HDFS using HiveQL
HiveQL is not exactly SQL, but very similar
Compiles down to MapReduce, later versions compile down to DAG (Tez)
Think of MapReduce as assembly language, there are various abstractions you can use above it which have their own advantages (HiveQL is one of them obviously)
Apache Pig is a procedural language that expresses processes on data and compiles down to MapReduce (scripts are called Pig Latin). You can write user defined functions in your own language (Javascript, for example) and use them in Pig

Apache HCatalog – Hortonworks distro only
Defines another way to look at your data files and figure out what files you want
HBase is another one

Workflow schedulers – Apache Oozie, LinkedIn Azkaban, Spotify Luigi are examples

The bad and the ugly:
* Data is not always local
* Still too much I/O
* Slow to compute
* Hard to make JobTracker High Availability (HA)
* Poor resource utilization (you can be either a mapper or a reducer)
* NameNodes are a single point of failure

YARN and MapReduce 2.0
YARN does cluster resource management. People call it an operating system for data processing. Improves on the issues with Hadoop above.

Apache Spark
Resilient Distributed Datasets (RDD) – represented as DAG (Directed Acyclic Graph)
Combine data and actions
Take data, transforms it, performs actions on it
RDD is split to do work in parallel as possible.
Transformation: map, filter, union, distinct, join, etc.
Actions: take, count, first, reduce, foreach, etc.
Works continously instead of in batches
Out of the box – Scala and Python
Has integration with Spark R, Spark SQL, Spark GraphX, Spark Streaming
Spark runs in clusters – can self-manage, or you can run in YARN or Apache Mesos
Driver program sends work to the cluster manager. Worker nodes do the work. Worker starts after the last processed data, so somewhat crash tolerant.
Spark has a large ecosystem of it’s own, similar to Hadoop

Stream Processing
Iterative batch processing (Deterministic batch operations)

Apache Storm
Handles streams
Takes from sources (spouts)
Processes in Bolts
Define a topology of Spouts and Bolts connected together
Runs continously, not batch

Apache Samza
Similar to Storm
Handle each message as it arrives
Garantees ordering

Data Pipes – how do we stream into Hadoop?
RabbitMQ, Cassandra, Redis, Kafka, etc.
Apache Flume

Zookeeper
Configuration Management, Synchronization

Since we are talking about distributed systems: read Aphyr’s “Call Me Maybe” blog series[https://aphyr.com/tags/jepsen]

ELK – Elastic Search, Logstash, Kafka to work with log streams

Apache Mahout – Machine learning framework

I am going to be posting my notes from each session I attend. These are raw and unedited, so I apologize for any grammar or spelling mistakes.

Building Highly Scalable Apps on the Azure Platform: Real World Guidance
#Kevin Grossnicklaus

You should sign up for an Azure account.
You get an Azure portal where you can access and create any kind of Azure service.
You can use bit and pieces – he sometimes uses Azure Service Bus with his AWS applications.
He puts an Azure deployment project in every Visual Studio solution.
He does not use the Azure emulator – he sets up a console app to run the service directly for development. It is just a wrapper around his Azure service. This is because it is faster to work with since it does not have to do the deployment, even to the local emulator.
He uses the Azure services that he needs from the console application (ASB, for example). This does require a constant internet connection, but it gives you full access to the services you need with all of the features. The emulator is not at parity with the real thing.
He believes very heavily in message-driven architectures. He uses MongoDB.
He has a logging service that takes in the log events and writes to a queue, and has a log writer service that actually writes to a DB.
All of the parts of Azure have libraries to support them available via NuGet if you are using .Net. There are SDKs available for non-.Net applications.
In order to work with other people, he appends things like the machine name or the environment to the front of the queue name so that messages aren’t stolen by the wrong computer or application.
Serialize small objects to the queue using a text format like JSON.
When you create Azure assets, you select what datacenter they will go in. Typically you want all of you stuff in the same datacenter to minimize latency (but what about redundancy – guess that is a different consideration and out of scope for this talk).
Send work via an async call to a queue to be done by a “backend server” if that work doesn’t need to be immediately visible to the user. For example, you might be able to do a simple form validation then queue up the form submission for later processing.
You can have multiple instances of a service running that listen to the same queue, and only the first one to pick it up will process it. If you want to have multiple consumers of the message, you would send it to a topic and all of the services that are interested in that topic would receive the message.
Now he is talking about precalculating things like a Facebook timeline. He didn’t use the terms “read model” or “eventual consistency” but that is what he is talking about here. He would use SignalR to send the updated read model to the client when it is ready.
Integrated Caching – use the memory in the servers you are already running instead of a dedicated cache. You can define the allocation. He allocates 1/3 of the memory for each of his web servers. This can save you money if you have the memory to spare. You can spin up dedicated cache – your application does not see any difference – it is just a different connection string. Redis is also available, but he doesn’t use it. It would require some code changes.
Azure Blob Store is like an unlimited disk. You put documents in containers, which can be public or private. You can use a public container as a CDN. It is probably better to wrap your blobs in an API instead of allowing direct access. This allows you to do things like reformatting (image size, for example).
He definitely spends a lot of time trying to figure out the best ways to use Azure cheaply, which makes sone sense.
He hosts MongoDB through mongolab, which actually runs on Azure so his databases end up in the same datacenter. You can even put the database in the same Azure network as the rest of your stuff. mongolab actually runs mongo on whichever cloud provider you want – it works with AWS as well.

I moved my blog to Github Pages using OctoPress. I thought it would be a better experience, but after nearly 2 years and only one post (which wasn’t worth much, so I didn’t bring it over), I realized I preferred WordPress so that I could just post stuff and not worry about the mechanics of it all. It also means I can post from pretty much any device at any time, so hopefully that helps.

So, my blog is back on WordPress (it actually never left – ekepes.wordpress.com has always worked, but it was out of date). Hopefully I will post more.

We are sizing and spec’ing hardware for a new product we are building. It is still on-premise (but could go to the cloud some day, honest!) so that means real honest to God servers. We were trying to decide on how best to do it while if possible not straying from what we typically ship for our other solutions. One of the servers will be a database server, meaning it will run SQL Server. All by itself – no other software on the box (this is a big deal for us – we generally just throw everything on one box and throw it out the door – best practices be damned).

When it came time to figure out what set of spindles to put TempDB on, I remembered that you could put it in RAM. The machine has a fair amount of RAM, so it was a reasonable thing to consider. Our manager of Core Technologies, whom the DBAs report up to, went to the DBAs to finalize the configuration plan with them. When he suggested putting TempDB in RAM, they looked at him in awe as if to say “are we really allowed to do that?” They had wanted to do it for a while, but were afraid to ask. They wanted to do something to make the system better, but never tried to see if they were allowed.

The morale of this story, always ask for the Moon. Don’t expect to get what you ask for, but if you don’t ask, you’ll never get it.