Big query - Command line tools and Tips - (MOSG)
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The document provides a comprehensive overview of Google BigQuery, a fully managed analytics data warehouse designed for petabyte-scale data analysis without the need for a database administrator. It details installation instructions, command line usage, tips for efficient data management, and pricing structures for storage and queries. Additionally, it discusses the advantages of using BigQuery for managing large datasets and offers sample queries and operations to get started.
![Activate your GPC account (1)
1. Preparation (create account)
2. Go to Google Cloud platform (has no account)
3. “Try IT Free”
https://cloud.google.com
nemo@ubuntu-14:~$ gcloud init
Welcome! This command will take you through the configuration of gcloud.
Your current configuration has been set to: [default]
To continue, you must log in. Would you like to log in (Y/n)?
Go to the following link in your browser:
https://accounts.google.com/o/oauth2/auth?redirect_uri=ur&xxxxxxxxxxxxx
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
access_type=offline
Enter verification code: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
You are now logged in as: [xxxx@example.com]
This account has no projects. Please create one in developers console (https://
console.developers.google.com/project) before running this command.
nemo@ubuntu-14:~$](https://image.slidesharecdn.com/bigquery2-160615091810/85/Big-query-Command-line-tools-and-Tips-MOSG-11-320.jpg)
![nemo@ubuntu-14:~$ gcloud init
Welcome! This command will take you through the configuration of gcloud.
Your current configuration has been set to: [default]
To continue, you must log in. Would you like to log in (Y/n)?
Go to the following link in your browser:
https://accounts.google.com/o/oauth2/auth?redirect_uri=ur&xxxxxxxxxxxxx
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
access_type=offline
Enter verification code: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
You are now logged in as: [xxxx@example.com]
This account has no projects. Please create one in developers console (https://
console.developers.google.com/project) before running this command.
nemo@ubuntu-14:~$
Activate your GPC account (2)](https://image.slidesharecdn.com/bigquery2-160615091810/85/Big-query-Command-line-tools-and-Tips-MOSG-12-320.jpg)
![https://accounts.google.com/o/oauth2/auth?redirect_uri=ur&xxxxxxxxxxxxxxxxx
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
access_type=offline
Enter verification code: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
You are now logged in as: [xxxx@example.com]
set the code
Activate your GPC account (6)](https://image.slidesharecdn.com/bigquery2-160615091810/85/Big-query-Command-line-tools-and-Tips-MOSG-16-320.jpg)
![https://accounts.google.com/o/oauth2/auth?redirect_uri=ur&xxxxxxxxxxxxxxxxx
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
access_type=offline
Enter verification code: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
You are now logged in as: [xxxx@example.com]
check the accounts
Activate your GPC account (7)](https://image.slidesharecdn.com/bigquery2-160615091810/85/Big-query-Command-line-tools-and-Tips-MOSG-17-320.jpg)
![Create table and import data (2)
Schema (schema.json)
[
{
"name":"id",
"type":"INTEGER"
},
{
"name":"name",
"type":"STRING"
},
{
"name":"engineer_type",
"type":"INTEGER"
}
]](https://image.slidesharecdn.com/bigquery2-160615091810/85/Big-query-Command-line-tools-and-Tips-MOSG-26-320.jpg)
Big query - Command line tools and Tips - (MOSG)
- 1. Google BigQuery - Command line and Tips - 2016/06/08 Mulodo Vietnam Co., Ltd.
- 2. What’s BigQuery Official site : https://cloud.google.com/bigquery/docs/ BigQuery is Google's fully managed, petabyte scale, low cost analytics data warehouse. BigQuery is NoOps—there is no infrastructure to manage and you don't need a database administrator—so you can focus on analyzing data to find meaningful insights, use familiar SQL, and take advantage of our pay-as-you-go model. → DWH: SQL like (easy to use), Petabyte scale(for Huge data)
- 3. Previous study “BigQuery - The First Step -“ (2016/05/26) • Just try to start for Google Big Query • Using query on the Google Cloud Platform console. • Create your own Dataset and Table • Using query for your table GPC console. http://www.meetup.com/Open-Study-Group-Saigon/events/231233151/ http://www.slideshare.net/nemo-mulodo/big-query-the-first-step-mosg c.f. “Big Data - Overview - “ http://www.slide http://www.meetup.com/Open-Study-Group-Saigon/events/229243903/ share.net/nemo-mulodo/big-data-overview-mosg
- 4. Command line tools and Tips 1. Preparation (install SDK and settings) 2. Try command line tools create datasets, tables and insert data. 3. Tips for business use. How to charge? Tips to reduce cost.
- 6. Preparation steps 1. Create “Google Cloud Platform(GCP)” account, and BigQuery. See) previous paper. 2. Install GCP SKD to your PC. (Using Ubuntu on Vagrant) 1. Installation 2. Activate your account 3. Set accounts for GCP SDK.
- 7. 2. Install GCP SKD 1. Installation
- 8. Install SKD to your PC. (1) nemo@ubuntu-14:~$ curl https://sdk.cloud.google.com | bash : Installation directory (this will create a google-cloud-sdk subdirectory) (/home/nemo): <-- Just type Enter (or you want) : Do you want to help improve the Google Cloud SDK (Y/n)? y : ! BigQuery Command Line Tool ! 2.0.24 ! < 1 MiB ! ! BigQuery Command Line Tool (Platform Specific)! 2.0.24 ! < 1 MiB ! : Modify profile to update your $PATH and enable shell command completion? (Y/n)? y (or you want) : For more information on how to get started, please visit: https://cloud.google.com/sdk/#Getting_Started nemo@ubuntu-14:~$ . ~/.bashrc <-- reload your bash environment nemo@ubuntu-14:~$
- 9. Install SKD to your PC. (2) // check the commands nemo@ubuntu-14:~$ which bq /home/nemo/google-cloud-sdk/bin/bq nemo@ubuntu-14:~$ which gcloud /Users/nemo/google-cloud-sdk/bin/gcloud
- 10. 2. Install GCP SKD 2. Activate your account
- 11. Activate your GPC account (1) 1. Preparation (create account) 2. Go to Google Cloud platform (has no account) 3. “Try IT Free” https://cloud.google.com nemo@ubuntu-14:~$ gcloud init Welcome! This command will take you through the configuration of gcloud. Your current configuration has been set to: [default] To continue, you must log in. Would you like to log in (Y/n)? Go to the following link in your browser: https://accounts.google.com/o/oauth2/auth?redirect_uri=ur&xxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx access_type=offline Enter verification code: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx You are now logged in as: [[email protected]] This account has no projects. Please create one in developers console (https:// console.developers.google.com/project) before running this command. nemo@ubuntu-14:~$
- 12. nemo@ubuntu-14:~$ gcloud init Welcome! This command will take you through the configuration of gcloud. Your current configuration has been set to: [default] To continue, you must log in. Would you like to log in (Y/n)? Go to the following link in your browser: https://accounts.google.com/o/oauth2/auth?redirect_uri=ur&xxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx access_type=offline Enter verification code: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx You are now logged in as: [[email protected]] This account has no projects. Please create one in developers console (https:// console.developers.google.com/project) before running this command. nemo@ubuntu-14:~$ Activate your GPC account (2)
- 13. https://accounts.google.com/o/oauth2/auth?redirect_uri=ur&xxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx access_type=offline Launch Browser Select accounts (if you already login with multiple accounts) Activate your GPC account (3)
- 16. https://accounts.google.com/o/oauth2/auth?redirect_uri=ur&xxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx access_type=offline Enter verification code: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx You are now logged in as: [[email protected]] set the code Activate your GPC account (6)
- 17. https://accounts.google.com/o/oauth2/auth?redirect_uri=ur&xxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx access_type=offline Enter verification code: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx You are now logged in as: [[email protected]] check the accounts Activate your GPC account (7)
- 18. Activate your GPC account (8) // set Project ID nemo@ubuntu-14:~$ gcloud config set project {{PROJECT_ID}} nemo@ubuntu-14:~$ // check the accounts nemo@ubuntu-14:~$ gcloud auth list - [email protected] (active) To set the active account, run: $ gcloud config set account ``ACCOUNT'' nemo@ubuntu-14:~$
- 19. What a pain! AWS is much easiler...
- 20. 2. Try command line tools
- 21. Try Public data (1) nemonemo@ubuntu-14:~$ bq show publicdata:samples.shakespeare Table publicdata:samples.shakespeare Last modified Schema Total Rows Total Bytes Expiration ----------------- ------------------------------------ ------------ ------------- ------------ 26 Aug 21:43:49 |- word: string (required) 164656 6432064 |- word_count: integer (required) |- corpus: string (required) |- corpus_date: integer (required) publicdata : samples . shakespeare {PROJECT_ID} : {DATASET} . {TABLE}
- 22. Try Public data (2) nemo@ubuntu-14:~$ bq query "SELECT word, COUNT(word) as count FROM publicdata:samples.shakespeare WHERE word CONTAINS 'raisin' GROUP BY word" Waiting on bqjob_r5e78fd2c80d5923c_000001554d1c4acc_1 ... (0s) Current status: DONE +---------------+-------+ | word | count | +---------------+-------+ | raising | 5 | | dispraising | 2 | | Praising | 4 | | praising | 7 | | dispraisingly | 1 | | raisins | 1 | +---------------+-------+ nemo@ubuntu-14:~$
- 23. Create Dataset (1) nemo@ubuntu-14:~$ bq ls <--- no dataset nemo@ubuntu-14:~$ bq mk saigon_engineers Dataset 'open-study-group-saigon:saigon_engineers' successfully created. nemo@ubuntu-14:~$ bq ls datasetId ------------------ <-- created!! saigon_engineers nemo@ubuntu-14:~$
- 24. Create Dataset (2) nemo@ubuntu-14:~$ bq ls <--- no dataset nemo@ubuntu-14:~$ bq mk saigon_engineers Dataset 'open-study-group-saigon:saigon_engineers' successfully created. nemo@ubuntu-14:~$ bq ls datasetId ------------------ <-- created!! saigon_engineers nemo@ubuntu-14:~$ Added!! -->
- 25. Create table and import data (1) name type ID INTEGER name STRING engineer_type INTEGER ID name type 1 nemo 1 2 miki 1 Schema Data
- 26. Create table and import data (2) Schema (schema.json) [ { "name":"id", "type":"INTEGER" }, { "name":"name", "type":"STRING" }, { "name":"engineer_type", "type":"INTEGER" } ]
- 27. Create table and import data (3) Data (data.json) {"id":1,"name":"nemo","engineer_type":1} {"id":2,"name":"miki","engineer_type":1}
- 28. Create table and import data (4) nemo@ubuntu-14:~$ bq load --source_format=NEWLINE_DELIMITED_JSON saigon_engineers.engineer_list data.json schema.json Upload complete. Waiting on bqjob_r23b898932d75d49a_000001554e5cae2f_1 ... (1s) Current status: DONE nemo@ubuntu-14:~$ bk load {PROJECT_ID}:{DATASET}.{TABLE} {data} {schema} Create table and import data https://cloud.google.com/bigquery/loading-data
- 29. Create table and import data (5) nemo@ubuntu-14:~$ bq load --source_format=NEWLINE_DELIMITED_JSON saigon_engineers.engineer_list data.json id:integer, name:string, engineer_type:integer Upload complete. Waiting on bqjob_r33b7802ea96b2c5d_000001554e4d21d5_1 ... (2s) Current status: DONE nemo@ubuntu-14:~$ Create table and import data : Another way
- 30. Create table and import data (6) nemo@ubuntu-14:~$ bq mk open-study-group- saigon:saigon_engineers.engineer_list schema.json nemo@ubuntu-14:~$ Create table bk mk {PROJECT_ID}:{DATASET}.{TABLE} {schema}
- 31. Create table and import data (7) nemo@ubuntu-14:~$ bq load --source_format=NEWLINE_DELIMITED_JSON saigon_engineers.engineer_list data.json Upload complete. Waiting on bqjob_r13717485c2c472e3_000001554e5b3ca3_1 ... (2s) Current status: DONE nemo@ubuntu-14:~$ Import data to database bk load {PROJECT_ID}:{DATASET}.{TABLE} {data}
- 32. Query (1) nemo@ubuntu-14:~$ bq show saigon_engineers.engineer_list Last modified Schema Total Rows Total Bytes Expiration ----------------- --------------------------- ------------ ------------- ------------ 14 Jun 10:02:35 |- id: integer 2 44 |- name: string |- engineer_type: integer nemo@ubuntu-14:~$
- 33. Query (2) nemo@ubuntu-14:~$ bq query "SELECT name FROM saigon_engineers.engineer_list" Waiting on bqjob_r12185d1aa88d92c8_0000015552d709d2_1 ... (0s) Current status: DONE +------+ | name | +------+ | nemo | | miki | +------+ nemo@ubuntu-14:~$
- 34. Query (3) nemo@ubuntu-14:~$ bq query --dry_run "SELECT name FROM saigon_engineers.engineer_list" Query successfully validated. Assuming the tables are not modified, running this query will process 12 bytes of data. nemo@ubuntu-14:~$ bk query --dry_run “QUERY” - get size of using memory before execution.
- 36. 3. Tips for business use
- 39. Column oriented (1) Sample case : database of Books ID (indexed) title (indexed) contents 1 The Cat Lorem ipsum dolor sit amet, consectetur (... 1.2MB) 2 Cats are love Lorem ipsum dolor sit amet, consectetur (... 1.5MB) 3 Littul Kittons Lorem ipsum dolor sit amet, consectetur (... 0.8MB) select id, title from books where name = ‘The Cat’
- 40. Column oriented (2) ID (indexed) title (indexed) contents 1 The Cat Lorem ipsum dolor sit amet, consectetur (... 1.2MB) 2 Cats are love Lorem ipsum dolor sit amet, consectetur (... 1.5MB) 3 Littul Kittons Lorem ipsum dolor sit amet, consectetur (... 0.8MB) select * from books where title = ‘The Cat’ @RDBMS index (name) hash data hash data hash data data in databaseIndexes scanned data
- 41. Column oriented (3) ID (indexed) title (indexed) contents 1 The Cat Lorem ipsum dolor sit amet, consectetur (... 1.2MB) 2 Cats are love Lorem ipsum dolor sit amet, consectetur (... 1.5MB) 3 Littul Kittons Lorem ipsum dolor sit amet, consectetur (... 0.8MB) select * from books where title = ‘The Cat’ @BigQuery data in database scanned data
- 42. Column oriented (3) ID (indexed) title (indexed) contents 1 The Cat Lorem ipsum dolor sit amet, consectetur (... 1.2MB) 2 Cats are love Lorem ipsum dolor sit amet, consectetur (... 1.5MB) 3 Littul Kittons Lorem ipsum dolor sit amet, consectetur (... 0.8MB) select * from books where title = ‘The Cat’ @BigQuery data in database scanned data Full-scan ANYTIME!!
- 43. Column oriented (4) ID (indexed) title (indexed) contents 1 The Cat Lorem ipsum dolor sit amet, consectetur (... 1.2MB) 2 Cats are love Lorem ipsum dolor sit amet, consectetur (... 1.5MB) 3 Littul Kittons Lorem ipsum dolor sit amet, consectetur (... 0.8MB) select * from books where title = ‘The Cat’ @BigQuery data in database If your database is Tera-byte scale, $5 per query !!!!
- 44. Column oriented (5) ID (indexed) title (indexed) contents 1 The Cat Lorem ipsum dolor sit amet, consectetur (... 1.2MB) 2 Cats are love Lorem ipsum dolor sit amet, consectetur (... 1.5MB) 3 Littul Kittons Lorem ipsum dolor sit amet, consectetur (... 0.8MB) select id, title from books where title = ‘The Cat’ @RDBMS index (name) hash data hash data hash data data in databaseIndexes scanned data
- 45. Column oriented (6) ID (indexed) title (indexed) contents 1 The Cat Lorem ipsum dolor sit amet, consectetur (... 1.2MB) 2 Cats are love Lorem ipsum dolor sit amet, consectetur (... 1.5MB) 3 Littul Kittons Lorem ipsum dolor sit amet, consectetur (... 0.8MB) select id, title from books where title = ‘The Cat’ @BigQuery data in database scanned data
- 46. Column oriented (6) ID (indexed) title (indexed) contents 1 The Cat Lorem ipsum dolor sit amet, consectetur (... 1.2MB) 2 Cats are love Lorem ipsum dolor sit amet, consectetur (... 1.5MB) 3 Littul Kittons Lorem ipsum dolor sit amet, consectetur (... 0.8MB) select id, title from books where title = ‘The Cat’ @BigQuery data in database scanned data Column Oriented
- 48. Table division Sample case : database of Books select id, title from books where time in ‘2016/06/17’ : : : : ID (indexed) title (indexed) contents time (indexed) 1 The Cat Lorem ipsum dolor sit amet, consectetur (... 1.2MB) 2016/01/01 00:00:00 2 Cats are love Lorem ipsum dolor sit amet, consectetur (... 1.5MB) 2016/01/01 00:01:23 353485397 Littul Kittons Lorem ipsum dolor sit amet, consectetur (... 0.8MB) 2016/06/17 00:01:46
- 49. Table division (1) index (time) hash data hash data hash data data in databaseIndexes scanned data : : : : ID (indexed) title (indexed) contents time (indexed) 1 The Cat Lorem ipsum dolor sit amet, consectetur (... 2016/01/01 00:00:00 2 Cats are love Lorem ipsum dolor sit amet, consectetur (... 2016/01/01 00:01:23 353485397 Littul Kittons Lorem ipsum dolor sit amet, consectetur (... 0.8MB) 2016/06/17 00:01:46 select id, title from books where time in ‘2016/06/17’ @RDBMS
- 50. Table division (2) data in database scanned data : : : : ID (indexed) title (indexed) contents time (indexed) 1 The Cat Lorem ipsum dolor sit amet, consectetur (... 1.2MB) 2016/01/01 00:00:00 2 Cats are love Lorem ipsum dolor sit amet, consectetur (... 1.5MB) 2016/01/01 00:01:23 353485397 Littul Kittons Lorem ipsum dolor sit amet, consectetur (... 0.8MB) 2016/06/17 00:01:46 select id, title from books where time in ‘2016/06/17’ @BigQuery Huge size
- 51. Table division (2) data in database scanned data : : : : ID (indexed) title (indexed) contents time (indexed) 1 The Cat Lorem ipsum dolor sit amet, consectetur (... 1.2MB) 2016/01/01 00:00:00 2 Cats are love Lorem ipsum dolor sit amet, consectetur (... 1.5MB) 2016/01/01 00:01:23 353485397 Littul Kittons Lorem ipsum dolor sit amet, consectetur (... 0.8MB) 2016/06/17 00:01:46 select id, title from books where time in ‘2016/06/17’ @BigQuery Huge size
- 52. Table division (3) ID (indexed) title (indexed) contents time (indexed) 1 The Cat Lorem ipsum dolor sit amet, consectetur (... 1.2MB) 2016/01/01 00:00:00 2 Cats are love Lorem ipsum dolor sit amet, consectetur (... 1.5MB) 2016/01/01 00:01:23 ID (indexed) title (indexed) contents time (indexed) 353485397 Littul Kittons Lorem ipsum dolor sit amet, consectetur (... 0.8MB) 2016/06/17 00:01:46 : Tables books_20160101 : books_20160617 Divide tables for each day.
- 53. Table division (4) ID (indexed) title (indexed) contents time (indexed) 1 The Cat Lorem ipsum dolor sit amet, consectetur (... 1.2MB) 2016/01/01 00:00:00 2 Cats are love Lorem ipsum dolor sit amet, consectetur (... 1.5MB) 2016/01/01 00:01:23 ID (indexed) title (indexed) contents time (indexed) 353485397 Littul Kittons Lorem ipsum dolor sit amet, consectetur (... 0.8MB) 2016/06/17 00:01:46 : books_20160101 : books_20160617 select id, title from books where time in ‘2016/06/17’ @BigQuery
- 54. Table division (5) ID (indexed) title (indexed) contents time (indexed) 1 The Cat Lorem ipsum dolor sit amet, consectetur (... 1.2MB) 2016/01/01 00:00:00 books_20160101 :: ID (indexed) title (indexed) contents time (indexed) 353485397 The Great Catsby Lorem ipsum dolor sit amet, consectetur (... 0.8MB) 2016/06/16 00:01:46 books_20160616 select id, title from books where time in ‘2016/06/16 - 2016/06/17’ @BigQuery ID (indexed) title (indexed) contents time (indexed) 353485397 Littul Kittons Lorem ipsum dolor sit amet, consectetur (... 2016/06/17 00:01:46 books_20160617
- 57. BigQuery is Fast Easy Cheap if it is used properly.
- 58. BigQuery is Fast Easy Cheap if it is used properly. Remember “--dry_run”
- 59. Thank you!