Sales were the topic of discussion. However, no other details were provided in the document beyond that one word, so an informative summary cannot be generated from the limited information given.
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- The problem
- The prevailing view vs. the practical reality
- A possible solution
- Or just building blocks?
- Nearness
- Near at hand, near to our skill set, near to our capabilities
- A more complete solution
- The PostgreSQL extension ecosystem
Agenda
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The Prevailing View - Logical
Dimension Relational Non-Relational
Schema objects ● Structured rows and columns
● Schema on write
● Referential integrity
● Painful migrations
● Unstructured files, docs, etc
● Schema on read
● No referential integrity
● No migrations
Query languages ● SQL
● Declarative
● Easy enough for non-tech users
● Various
● Procedural
● Requires some programming skills
Exploratory analysis ● Native support for joins
● Interactive/low execution overhead
● No native support for joins
● OLAP - Batch processing
Data science and ML ● Only descriptive statistics
● Requires exporting dumps/samples
● Robust ecosystem
● Does not require exports
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The Prevailing View - Physical
Dimension Relational Non-Relational
Parallel query
processing
● Single node system
● Single process per query
● Multiple node system
● Multiple processes per query
Concurrency ● High concurrency
● Single process per connection
● OLAP - low concurrency/high
scheduling overhead
High Availability &
Replication
● Async and sync replication
● HA may not be native
● Async and sync replication
● HA likely to be native
Sharding ● Sharding may not be native
● Difficult to manage
● Sharding likely to be native
● Easy to manage
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The Prevailing View - Summary
- RDBMS have nice properties for producing rich data
- ACID, relational integrity, constraints, strong data types
- Easier for non-tech users and exploratory analysis
- Probably don’t meet the needs of today’s analysts
- Data science & Machine Learning
- Parallel processing
- Definitely don’t meet the needs of today’s apps
- Schema migrations
- Replication and sharding
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Modern SQL
- Many people still think of SQL in terms of SQL-92
- Since then we’ve had: SQL:1999, SQL:2003, SQL:2006, SQL:2008,
SQL:2011
- http://use-the-index-luke.com/blog/2015-02/modern-sql
- Common Table Expressions (CTEs) / Recursive CTEs
- Window Functions
- Ordered-set Aggregates
- Lateral joins
- Temporal support
- The list goes on...
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- Near at hand
- Easily installable
- Near to our skill set
- Familiar tool/language/abstraction
- Modular and composable
- Near to our capabilities
- Capable of solving a problem in our domain
Nearness Drives Adoption
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UDAs & Data Types: postgresql-hll
- Near to our capabilities & near to our skill set
- Data type
- Estimate count distinct with tunable precision
- 1280 bytes estimates tens of billions of distinct values with few percent error
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Beyond Analytics
- Web app framework
- http://blog.aquameta.com/
- REST API
- https://github.com/begriffs/postgrest
- Unit testing framework
- http://pgtap.org/
- Firewall
- https://github.com/uptimejp/sql_firewall
- More every week!
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Conclusion
- With PostgreSQL, you get
- more than rows and columns
- more than SELECT, FROM, WHERE, GROUP BY, ORDER BY
- more than a single machine
- Make sure you get the full return on your investment!
Get your Chartio free trial!
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