GCP Study Notes: Tips collections

What's the difference between Kubernetes and Docker?

There are two kinds of usage: committed and sustained discount usage.

vCPU = unit of measure of CPU.

RAM is measure in unit of Gigabite.

There are 3 different ways to do dev on GCP: command line(gcloud), REST API, Google Cloud Consule.

HDD: harddisk device. SSD: Solid State Device. SSD is more expensive than HDD, physically more closely to the VM.

2 kinds of VM: standard and preemptible: preempitble instracne are more cheap, technically the same as standard, but charged on the excess usage,
is always terminated after 24 hours and when the GCP usage spikes, also terminates, whichever comes first. This is the reason they might be stopped at any time.


Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications.
Kubernetes was built by Google based on their experience running containers in production using an internal cluster management system called Borg.


In GCP: Compute engine ~ Virtual Machine = AWS (EC2)

Here is some example using papermill to run a collection of jupyter notebooks automatically, output to GCS, and then shut down to save the cost.
The following command starts execution of a Jupyter notebook stored in a Cloud Storage bucket:

#===============================================================
# Compute Engine Instance parameters
export IMAGE_FAMILY="tf-latest-cu100" 
export ZONE="us-central1-b"
export INSTANCE_NAME="notebook-executor"
export INSTANCE_TYPE="n1-standard-8"
# Notebook parameters
export INPUT_NOTEBOOK_PATH="gs://my-bucket/input.ipynb"
export OUTPUT_NOTEBOOK_PATH="gs://my-bucket/output.ipynb"
export PARAMETERS_FILE="params.yaml" # Optional
export PARAMETERS="-p batch_size 128 -p epochs 40"  # Optional
export STARTUP_SCRIPT="papermill ${INPUT_NOTEBOOK_PATH} ${OUTPUT_NOTEBOOK_PATH} -y ${PARAMETERS_FILE} ${PARAMETERS}"
gcloud compute instances create $INSTANCE_NAME \
        --zone=$ZONE \
        --image-family=$IMAGE_FAMILY \
        --image-project=deeplearning-platform-release \
        --maintenance-policy=TERMINATE \
        --accelerator='type=nvidia-tesla-t4,count=2' \
        --machine-type=$INSTANCE_TYPE \
        --boot-disk-size=100GB \
        --scopes=https://www.googleapis.com/auth/cloud-platform \
        --metadata="install-nvidia-driver=True,startup-script=${STARTUP_SCRIPT}"
gcloud --quiet compute instances delete $INSTANCE_NAME --zone $ZONE
#===============================================================

The above commands do the following:

Create a Compute Engine instance using TensorFlow Deep Learning VM and 2 NVIDIA Tesla T4 GPUs
Install the latest NVIDIA GPU drivers
Execute the notebook using Papermill

Upload notebook result (with all the cells pre-computed) to Cloud Storage bucket in this case: “gs://my-bucket/”

Terminate the Compute Engine instance
After clicking activate google cloud Shell, in the terminal, you can view your roles by running:

gcloud iam roles describe roles/viewer
gcloud iam roles describe roles/editor
gcloud iam roles describe roles/owner

Google Cloud Dataproc is a fast, easy-to-use, fully-managed cloud service for running Apache Spark and Apache Hadoop clusters in a simple, cost-efficient way.
Cloud Dataproc easily integrates with other Google Cloud Platform (GCP) services, giving you a powerful and complete platform for data processing, analytics and machine learning

Apache Spark is an analytics engine for large scale data processing. Logistic regression is available as a module in Apache Spark's machine learning library, MLlib.
Spark MLlib, also called Spark ML, includes implementations for most standard machine learning algorithms such as k-means clustering, random forests,
alternating least squares, k-means clustering, decision trees, support vector machines, etc. Spark can run on a Hadoop cluster, like Google Cloud Dataproc, in order to process very large datasets in parallel.

Relational databases are also called Relational Database Management Systems (RDBMS) or SQL databases. Historically,
the most popular of these have been Microsoft SQL Server, Oracle Database, MySQL, and IBM DB2.
Relational databases are designed to run on a single server in order to maintain the integrity of the table mappings and avoid the problems of distributed computing.

Non-relational databases are also called NoSQL databases. NoSQL has become an industry standard term,
but the name is beginning to lose popularity since it doesn’t fully cover the complexity and range of non-relational data stores that are available.
Some of the most known NoSQL or non-relational DBs that Serra discussed are MongoDB, DocumentDB, Cassandra, Coachbase, HBase, Redis, and Neo4j.

Hadoop is a file system with components made up of Hadoop Distributed File System (HDFS), Yarn, and MapReduce.

Cloud Spanner is the first scalable, enterprise-grade, globally-distributed, and strongly consistent database service built for the cloud specifically to combine the benefits of relational database structure with non-relational horizontal scale.

Difference between horizontally scaling and vertically scaling:

In simple layman language, Horizontal scaling is more complex than the vertical scaling(adding more cpu,memory at the existing machine).

Horizontal scaling means that you scale by adding more machines into your pool of resources
whereas Vertical scaling means that you scale by adding more power (CPU, RAM) to an existing machine.

Horizontal Scaling, also referred to as "scale-out" is basically the addition of more machines or setting up a cluster or a distributed environment for your software system.
This usually requires a load-balancer program which is a middle-ware component in the standard 3 tier client-server architectural model.

Vertical Scaling - also referred to as "scale-up" approach is an attempt to increase the capacity of a single machine :
By adding more processing power By adding more storage More memory etc.

Horizontal scaling comes with overhead in the form of cluster setup, management, and maintenance costs and complexities.
The design gets increasingly complex and programming model changes.

Relational Databases:

Pros: Relational databases work with structured data.
They support ACID transactional consistency and support “joins.”
They come with built-in data integrity and a large eco-system.
Relationships in this system have constraints.
There is limitless indexing. Strong SQL.

Cons:
Relational Databases do not scale out horizontally very well (concurrency and data size), only vertically, (unless you use sharding).
Data is normalized, meaning lots of joins, which affects speed.
They have problems working with semi-structured data.

Non-relational/NoSQL
Pros:
They scale out horizontally and work with unstructured and semi-structured data. Some support ACID transactional consistency.
Schema-free or Schema-on-read options.
High availability.
While many NoSQL databases are open source and so “free”, there are often considerable training, setup, and developments costs. There are now also numerous commercial products available.

Cons:
Weaker or eventual consistency (BASE) instead of ACID.
Limited support for joins.
Data is denormalized, requiring mass updates (i.e. product name change).
Does not have built-in data integrity (must do in code).
Limited indexing.

What is SSH?
Secure Shell, sometimes referred to as Secure Socket Shell, is a protocol which allows you to connect securely to a remote computer or a server by using a text-based interface.

When a secure SSH connection is established, a shell session will be started, and you will be able to manipulate the server by typing commands within the client on your local computer.


Question:
ImportError: No module named google.cloud

Answer:
1. pip install --upgrade google-cloud-storage
2. restart the kerkel.

if you run the following: pip install google-cloud, it might not work.


Question:
What's the product matching between AWS and GCP?
Answer:

Service Category	Service	AWS	Google Cloud Platform
Compute	IaaS	Amazon Elastic Compute Cloud(EC2)	Compute Engine
	PaaS	AWS Elastic Beanstalk	App Engine
	Containers	Amazon Elastic Container Service	Google Kubernetes Engine
	Serverless Functions	AWS Lambda	Cloud Functions
	Managed Batch Computing	AWS Batch	N/A
Network	Virtual Networks	Amazon Virtual Private Cloud	Virtual Private Cloud
	Load Balancer	Elastic Load Balancer	Cloud Load Balancing
	Dedicated Interconnect	Direct Connect	Cloud Interconnect
	Domains and DNS	Amazon Route 53	Google Domains, Cloud DNS
	CDN	Amazon CloudFront	Cloud CDN
Storage	Object Storage	Amazon Simple Storage Service(S3)	Cloud Storage
	Block Storage	Amazon Elastic Block Store	Persistent Disk
	Reduced-availability Storage	Amazon S3 One Zone-Infrequent Access	Cloud Storage Nearline
	Archival Storage	Amazon Glacier	Cloud Storage Coldline
	File Storage	Amazon Elastic File System	Cloud Filestore (beta)
Database	RDBMS	Amazon Aurora	Cloud SQL, Cloud Spanner
	NoSQL: Key-value	Amazon DynamoDB	Cloud Firestore, Cloud Bigtable
	NoSQL: Indexed	Amazon SimpleDB	Cloud Firestore
Big Data & Analytics	Batch Data Processing	Amazon Elastic MapReduce, AWS Batch	Cloud Dataproc, Cloud Dataflow
	Stream Data Processing	Amazon Kinesis	Cloud Dataflow
	Stream Data Ingest	Amazon Kinesis	Cloud Pub/Sub
	Analytics	Amazon Redshift, Amazon Athena	BigQuery
	Workflow Orchestration	Amazon Data Pipeline, AWS Glue	Cloud Composer
Application Services	Messaging	Amazon Simple Notification Service	Cloud Pub/Sub
Management Services	Monitoring	Amazon CloudWatch	Stackdriver Monitoring
	Logging	Amazon CloudWatch Logs	Stackdriver Logging
	Deployment	AWS CloudFormation	Cloud Deployment Manager
Machine Learning	Speech	Amazon Transcribe	Cloud Speech-to-Text
	Vision	Amazon Rekognition	Cloud Vision
	Natural Language Processing	Amazon Comprehend	Cloud Natural Language
	Translation	Amazon Translate	Cloud Translation
	Conversational Interface	Amazon Lex	Dialogflow Enterprise Edition
	Video Intelligence	Amazon Rekognition Video	Cloud Video Intelligence
	Auto-generated Models	N/A	Cloud AutoML (beta)
	Fully Managed ML	Amazon SageMaker	Cloud Machine Learning Engine

Running Jupyter Notebook on Google Cloud Platform in 15 min

Here is example of create table in google bigquery:

#===========================================================
create table `maximal-inkwell-#####.sample1.test`  
PARTITION BY myDate
CLUSTER BY cluster_col 
as 
SELECT * FROM `bigquery-public-data.usa_names.usa_1910_2013` LIMIT 10


{CREATE TABLE | CREATE TABLE IF NOT EXISTS | CREATE OR REPLACE TABLE}
table_name
[(
  column_name column_schema[, ...]
)]
[PARTITION BY partition_expression]
[CLUSTER BY clustering_column_list]
[OPTIONS(table_option_list)]
[AS query_statement]


##Here are the python code: 
import time

from google.cloud import bigquery
from google.cloud.bigquery.table import Table
from google.cloud.bigquery.dataset import Dataset

def create_table_as_select(dataset_name, table_name, sqlQuery, project=None):
    try:
    job_config = bigquery.QueryJobConfig()

    # Set configuration.query.destinationTable
    dataset_ref = bigquery_client.dataset(dataset_name)
    table_ref = dataset_ref.table(table_name)

    job_config.destination = table_ref

    # Set configuration.query.createDisposition
    job_config.create_disposition = 'CREATE_IF_NEEDED'

    # Set configuration.query.writeDisposition
    job_config.write_disposition = 'WRITE_APPEND'

    # Start the query
    job = bigquery_client.query(sqlQuery, job_config=job_config)

    # Wait for the query to finish
    job.result()

    returnMsg = 'Created table {} .'.format(table_name)

    return returnMsg

except Exception as e:
    errorStr = 'ERROR (create_table_as_select): ' + str(e)
    print(errorStr)
    raise
#===========================================================

#===========================================================
# Create a dataset using bigquery API:
bigquery_client  = bigquery.Client() #Create a BigQuery service object
dataset_id = 'my_dataset' 
dataset_ref = bigquery_client.dataset(dataset_id) # Create a DatasetReference using a chosen dataset ID.
dataset = bigquery.Dataset(dataset_ref)  # Construct a full Dataset object to send to the API.
dataset.location = 'US' # Specify the geographic location where the new dataset will reside. 
Remember this should be same location as that of source data set from where we are getting data to run a query

# Send the dataset to the API for creation. Raises google.api_core.exceptions.AlreadyExists if the Dataset already exists within the project.
dataset = bigquery_client.create_dataset(dataset)  # API request
print('Dataset {} created.'.format(dataset.dataset_id))

#===========================================================