This document provides information about database bloat and performance tuning. It introduces Denish Patel, a database architect with expertise in heterogeneous databases including PostgreSQL, Oracle and MySQL. The document discusses what causes database bloat, issues it can create, and tools for identifying, measuring and removing bloat. These include vacuum, vacuum full, cluster, pg_bloat_report, check_postgres_bloat, compact_table and pg_reorg. Monitoring and prevention techniques are also covered.
This document provides information about database bloat and performance tuning. It introduces Denish Patel, a database architect with expertise in heterogeneous databases including PostgreSQL, Oracle and MySQL. The document discusses what causes database bloat, issues it can create, and tools for identifying, measuring and removing bloat. These include vacuum, vacuum full, cluster, pg_bloat_report, check_postgres_bloat, compact_table and pg_reorg. Monitoring and prevention techniques are also covered.
AWS Webcast - Achieving consistent high performance with Postgres on Amazon W...Amazon Web Services
Postgres is a popular relational database and is the backend of a number of high traffic applications. Join AWS and PalominoDB, the company that helped Obama for America campaign optimize the database infrastructure on AWS, to learn about how you can run high throughput, I/O intensive Postgres clusters on the Amazon EBS storage platform. We will go over best practices including performance, durability and optimization related to deploying Postgres on AWS.
You hear about the best practices learned and applied for the Obama for America campaign.
In this webinar, you will learn about:
- Amazon Elastic Block Store (EBS)
- Why Provisioned IOPS volumes fit the needs of high I/O intensive applications
- Best practices for deploying Postgres on AWS
- How to leverage Provisioned IOPS volumes for Postgres
This document discusses advanced Postgres monitoring. It begins with an introduction of the speaker and an agenda for the discussion. It then covers selection criteria for monitoring solutions, compares open source and SAAS monitoring options, and provides examples of collecting specific Postgres metrics using CollectD. It also discusses alerting, handling monitoring changes, and being prepared to respond to incidents outside of normal hours.
This document discusses using PostgreSQL with Amazon RDS. It begins with an introduction to Amazon RDS and then discusses setting up a PostgreSQL RDS instance, available features like backups and monitoring, limitations, pricing, and references for further reading. The document is intended to provide an overview of deploying and managing PostgreSQL on Amazon RDS.
6 Valuable Lessons on How to Turn a Profit - @cnbc @marcuslemonis #TheProfitEmpowered Presentations
How to turn a profit. 6 valuable lessons learned from season one of the hit tv show The Profit starring marcus lemonis on CNBC. Of the 6 struggling businesses Marcus attempted to help, 3 were a success, and 3 were not.
we can learn from all of them.
An Overview of Designing Microservices Based Applications on AWS - March 2017...Amazon Web Services
Microservices are an architectural approach to decompose complex applications into smaller, independent services. AWS customers benefit from increased agility, simplified scalability, resiliency, and faster deployments by migrating from monoliths to microservices based architecture.
In this session, we will provide an overview of the benefits and challenges of microservices, and share best practices for architecting and deploying microservices on AWS. We will dive into different approaches you can take to run microservices applications at scale and explore how services like Amazon ECS, AWS Lambda, and AWS X-Ray make it simpler to design and maintain these applications.
Learning Objectives:
1. Understand the fundamentals of the microservices architectural approach
2. Learn best practices for designing microservices on AWS
3. Learn the basics of Amazon EC2 Container Service, AWS Lambda, and AWS X-Ray
Presentation given by CEO Jeff Weiner, and CFO Steve Sordello, at LinkedIn Q4 2015 Earnings Call. For more information, check out http://investors.linkedin.com/.
Tired of losing sales pitches? Look no further, get some timeless advice from high-stakes presentation consultant: Cliff Atkinson on how to throw out your old sales pitch and make your next one count.
Download here: http://www.paywithapost.de/pay?id=80eb8437-7393-4e61-b8a6-175d76d9eb5b
UX, ethnography and possibilities: for Libraries, Museums and ArchivesNed Potter
1) The document discusses how the University of York Library has used various user experience (UX) techniques like ethnographic observation and interviews to better understand user needs and behaviors.
2) Some changes implemented based on UX findings include installing hot water taps, changing hours, and adding blankets - aimed at improving the small details of user experience.
3) The presentation encourages other libraries, archives and museums to try incorporating UX techniques like behavioral mapping and cognitive interviews to inform design changes that enhance services for users.
An immersive workshop at General Assembly, SF. I typically teach this workshop at General Assembly, San Francisco. To see a list of my upcoming classes, visit https://generalassemb.ly/instructors/seth-familian/4813
I also teach this workshop as a private lunch-and-learn or half-day immersive session for corporate clients. To learn more about pricing and availability, please contact me at http://familian1.com
5. 前方最長完全一致検索
• クエリ例
5
SELECT * FROM test;
col1
--------
A
BC
ABC
ABCD
ABCDEF
SELECT * FROM test WHERE col1 IN
(SELECT left('ABCDE',generate_series(1,length('ABCDE'))))
ORDER BY length(col1) DESC LIMIT 1;
col1
------
ABCD
left
-------
A
AB
ABC
ABCD
ABCDE
最長の完全一致を取得
6. 文字列のソート
• order byでの列ソートではなく文字列毎のソート
• つまり
「PQODJHAZBDVACWKABOWPX」を
「AAABBCDDHJKOOPPQVWWXZ」とする
6
8. 文字列のソート
• 途中クエリ2
8
SELECT * FROM test;
col1
------
SQL
DATA
SELECT col1,substr(col1,generate_series(1,length(col1)),1) sub
FROM test ORDER BY col1,sub;
col1 | sub
------+-----
DATA | A
DATA | A
DATA | D
DATA | T
SQL | L
SQL | Q
SQL | S
1文字ずつ分解してsort
9. 文字列のソート
• クエリ例
9
SELECT * FROM test;
col1
-----------------------
PQODJHAZBDVACWKABOWPX
UDFPGIEHAPTNVUDIIEHAO
SELECT col1,array_to_string(array_agg(sub),'') sorted FROM
(SELECT col1,substr(col1, generate_series(1,length(col1)),1) sub
FROM test ORDER BY col1,sub) foo GROUP BY col1;
col1 | sorted
-----------------------+-----------------------
PQODJHAZBDVACWKABOWPX | AAABBCDDHJKOOPPQVWWXZ
UDFPGIEHAPTNVUDIIEHAO | AADDEEFGHHIIINOPPTUUV
10. 欠番探し
• クエリ例
10
SELECT id FROM test;
id
---------
ID00005
ID00006
ID00008
ID00009
ID00010
SELECT id FROM (SELECT 'ID' || lpad(generate_series(1,10)::text,5,'0') id)
foo WHERE id NOT IN (SELECT id FROM test);
id
---------
ID00001
ID00002
ID00003
ID00004
ID00007
id
---------
ID00001
ID00002
ID00003
ID00004
ID00005
ID00006
ID00007
ID00008
ID00009
ID00010
文字列連結、lpad、
castを併用