Google Cloud Platform (GCP) - How do I choose among the Storage and Big Data options?

Storage options are extremely important when using a GCP, performance and price wise. I will do a bit of a non-standard approach for this post. I will first cover the potential use cases, explain the Hadoop/Standard DB you would use in each case, and then the GCP option for the same use case. Once that part is done, I will go a bit deeper into each of GCP Storage and Big Data technologies. This post will therefore have 2 parts, and an "added value" Anex:
  1. Which option fits to my use case?
  2. Technical details on GCP Storage and Big Data technologies
  3. Added Value: Object Versioning and Life Cycle management

1. Which option fits to my use case?

Before we get into the use cases, let's make sure we understand the layers of abstraction of Storage. Block Storage is a typical storage carried out by applications, data stored in cylinders, UNSTRUCTURED DATA WITH NO ABSTRACTION. When you can refer to data using a physical address - you're using Block Storage. You would normally need some abstraction to use the storage, it would be rather difficult to reference your data by blocks. File Storage is a possible abstraction, and it means you are referring to data using a logical address. In order to do this, we will need some kind of layer on top of our blocks, an intelligence to make sure that our blocks underneath are properly organized and stored in the disks, so that we don't get the corrupt data.

Let's now focus on the use cases, and a single question - what kind of data do you need to store?

If you're using Mobile, the you will be using a slightly different data structures:

Let's now get a bit deeper into each of the Use Cases, and see what Google Cloud can offer.
  1. If you need Block Storage for your compute VMs/instances, you would obviously be using a Googles IaaS option called Compute Engine (GCE), and you would create the Disks using:
    • Persistent disks (Standard or SSD)
    • Local SSD
  1. If you need to store an unstructured data, or "Blobs", as Azure calls it, such as Video, Images and similar Multimedia Files - what you need is a Cloud Storage.
  2. If you need your BI guys to access your Big Data using an SQL like interface, you'll use a BigQuery, a Hive-like Google product. This applies to cases 3 (SQL interface required), and 7 (OLAP/Data Warehouse).
  3. To store the NoSQL Documents like HTML/XML, that have a characteristic pattern, you should use DataStore.
  4. For columnar NoSQL data, that requires fast scanning, use BigTable (GCP equivalent of HBase).
  5. For Transactional Processing, or OLTP , you should use Cloud SQL (if you prefer open source) or Cloud Spanner (if you need less latency, and horizontal scaling).
  6. Same like 3.
  7. Cloud Storage for Firebase is great for Security when you are doing Mobile.
  8. Firebase Realtime DB is great for fast random access with mobile SDK. This is a NoSQL database, and it remains available even when you're offline.

2. Technical details on GCP Storage and Big Data technologies

Storage - Google Cloud Storage

Google Cloud Storage is created in the form of BUCKETS, that are globally unique, identified by NAME, more or less like a DNS. Buckets are STANDALONE, not tied to any Compute or other resources.

TIP: If you want to use Cloud Storage with a web site, have in mind that you need a Domain Verification (adding a meta-tag, uploading a special HTML file or directly via the Search Console).

There are 4 types of Bucket Storage Classes. You need to be really careful to choose the most optimal Class for your Use Case, because the ones that are designed not used frequently are the ones where you'll be charged per access.  You CAN CHANGE a Buckets Storage class. The files stored in the Bucket are called OBJECTS, the Objects can have the Class which is same or "lower" then the Bucket, and if you change the Bucket storage class - the Objects will retain their storage class. The Bucket Storage Classes are:
  • Multi-regional, for frequent access from anywhere around the world. It's used for "Hot Objects", such as Web Content, it has a 99,95% availability, and it's Geo-redundant. It's pretty expensive, 0.026/GB/Month.
  • Regional, frequent access from one region, with 99,9% availability, appropriate for storing data used by Cloud Engine instances. Regilnal class has performance for data intensive computations, unlike multi-regional.
  • Nearline - access once at month at max, with 99% availability, costing 0.01/GB/month with a 30 day minimum duration, but it's got ACCESS CHARGES. It can be used for data Backup, DR or similar.
  • Coldline - access once a year at max, with same throughput and latency, for 0.007/GB/month with a 90 day minimum duration, so you would be able to retrieve your backup super fast, but you would get a bit higher bill.. At least your business wouldn’t suffer.

We can get a data IN and OUT of Cloud Storage using:
  • XML and JSON APIs
  • Command Line (gsutil - a command line tool for storage manipulation)
  • GSP Console (web)
  • Client SDK

You can use TRANSFER SERVICE in order to get your date INTO the Cloud Storage (not out!), from AWS S3, http/https, etc. This tool won't let you get the data out. Basically you would use:
  • gsutil when copying files for the first time from on premise.
  • Transfer Service when transferring from AWS etc.

Cloud Storage is not like Hadoop in the architecture sense, mostly because a HDFS architecture requires a Name Node, which you need to access A LOT, and this would increase your bill. You can read more about Hadoop and it's Ecosystem in my previous post, here.

When should I use it?

When you want to store UNSTRUCTURED data.

Storage - Cloud SQL and Google Spanner

These are both relational databases, super structured data. Cloud Spanner offers ACID++, meaning it's perfect for OLTP. It would, however, be too slow and too many checks for Analytics/BI (OLAP), because OLTP needs strict write consistency, OLAP does not. Cloud Spanner is Google proprietary, and it offers horizontal scaling, like bigger data sets.

*ACID (Atomicity, Consistency, Isolation, Durability) is a set of properties of database transactions intended to guarantee validity even in the event of errors, power failures, etc.

When should I use it?

OLTP (Transactional) Applications.

Storage - BigTable (Hbase equivalent)

BigTable is used for FAST scanning of SEQUENTIAL key values with LOW latency (unlike Datastore, which would be used for non-sequential data). Bigtable is a columnar database, good for sparse data (meaning - missing fields in the table), because similar data is stored next to each other. ACID properties apply only on the ROW level.

What is columnar Data Base? Unlike RDBMS, it is not normalised, and it is perfect for Sparse data (tables with bunch of missing values, because the Columns are converted into rows in the Columnar data store, and the Null value columns are simply not converted. Easy.). Columnar DBs are also great for the data structures with the Dynamic Attributes because we can add new columns without changing the schema.

Bigtable is sensitive to hot spotting.

When should I use it?

Low Latency, SEQUENTIAL data.

Storage - Cloud Datastore (has similarities to MongoDB)

This is much simpler data store then BigTable, similar to MongoDB and CouchDB. It's a key-value structure, like structured data, designed to store documents, and it should not be used for OLTP or OLAP but instead for fast lookup on keys (needle in the haystack type of situation, lookup for non sequential keys).  Datastore is similar to RDBMS in that they both use indices for fast lookups. The difference is that DataStore query execution time depends on the size of returned result, so it will take the same time if you're querying a dataset of 10 rows or 10.000 rows.

IMPORTANT: Don’t use DataStore for Write intensive data, because the indices are fast to read, but slow to write.

When should I use it?

Low Latency, NON-SEQUENTIAL data (mostly Documents that need to be searched really quickly, like XML or HTML, that has a characteristic patterns, to which Datastore is performing INDEXING). It's perfect for SCALING of a HIARARCHICAL documents with Key/Value data. Don't use DataStore if you're using OLTP (Cloud Spanner is a better. choice) or OLAP/Warehousing (BigQuery is a better choice). Don't use for unstructured data (Cloud Storage is better here). It's good for Multi Tenancy (think of HTML, and how the schema can be used to separate data).

Big Data - Dataproc

Dataproc is a GCP managed Hadoop + Spark (every machine in the Cluster includes Hadoop, Hive, Spark and Pig. You need at lease 1 master and 2 workers, and other workers can be Preemptable VMs). Dataproc uses Google Cloud Storage instead of HDFS, simply because the Hadoop Name Node would consume a lot of GCE resources.

When should I use it?

Dataproc allows you to move your existing Hadoop to the Cloud seamlessly.

Big Data - Dataflow

In charge of transformation of data, similar to Apache Spark in Hadoop ecosystem. Dataflow is based on Apache Beam, and it models the flow (PIPELINE) of data and transforms it as needed. Transform takes one or more Pcollections as input, and produces an output Pcollection.

Apache Beam uses the I/O Source and Sink terminology, to represent the original data, and the data after the transformation.

When should I use it?

Whenever you have one data format on the Source, and you need to deliver it in a different format, as a Backend you would use something like Apache Spark or Dataflow.

Big Data - BigQuery

BigQuery is not designed for the low latency use, but it is VERY fast comparing to Hive. It's not as fast as Bigtable and Datastore which are actually preferred for low latency. BigQuery is great for OLAP, but it cannot be used for transactional processing (OLTP).

When should I use it?

If you need a Data Warehouse if your application is OLAP/BA or if you require an SQL interface on top of Big Data.

Big Data - Pub/Sub

Pub/Sub (Publisher/Subscriber) is a messaging transport system. It can be defined as messaging Middleware. The subscribers subscribe to the TOPIC that the publisher publishes, after which the Subscriber sends an ACK to the "Subscription", and the message is deleted from the source. This message stream is called the QUEUE. Message = Data + Attributes (key value pairs). There are two types of subscribers:
  • PUSH Subscriber, where the Apps make HTTPS request to
  • PULL Subscriber, where the Web Hook endpoints able to accept POST requests over HTTPS

When should I use it?

Perfect for applications such as Oder Processing, Event Notifications, Logging to multiple systems, or maybe Streaming data from various Sensors (typical for IoT).

Big Data - Datalab

Datalab is an environment where you can execute notebooks. It's basically a Jupyter or iPhython for notebooks for running code. Notebooks are better the text files for Code, because they include Code, Documentation (markdown) and Results. Notebooks are stored in Google Cloud Storage.

When should I use it?

When you want to use Notebooks for your code.

Need some help choosing?

If it's still not clear which is the best option for you, Google also made a complete Decision Tree, exactly like in the case of "Compute".

3. Added Value: Object Versioning and Lifecycle Management

Object Versioning

By default in Google Cloud Storage If you delete a file in a Bucket, the older file is deleted, and you can't get it back. When you ENABLE Object Versioning on a Bucket (can only be enabled per bucket), the previous versions are ARCHIVED, and can be RETRIEVED later.

When versioning is enabled, you can perform different actions, for example - use an older file and override the LIVE version, or similar.

Object Lifecycle Management

To avoid the archived version creating a chaos in some point of time, it's recommendable to implement some kind of Lifecycle Management. The previous versions of the file maintain their own ACL permissions, which may be different then the LIVE one.

Object Lifecycle Management can turn on the TTL. You can create CONDITIONS or RULES to base your Object Versioning. This can get much more granular, because you have:
  • Conditions are criteria that must be met before the action is taken. These are: Object age, Date of Creation, If it's currently LIVE, Match a Storage Class, and Number of Newer Versions.
  • Rules
  • Actions, you can DELETE or Set another Storage Class.

This way you can get pretty imaginative, and for example delete all objects older then 1 year, or perhaps if a Rule is triggered and conditions are met - change the Class of the Object from, for example, Regional to Nearline etc.

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