Quick Lesson on Databases

• Relational databases are key to managing complex data

• You’ve been using relational databases with “Joins” and “Relates” in ArcGIS

• GeoDatabases are relational databases

• Structured Query Language (SQL) is the primary language for relational databases

• You’ve been using SQL statements in ArcGIS to query data

Relational Databases

• Need to represent data with a complex structure

Plot

TreeSpecies

Database Tables

• What you’ve seen in ArcGIS only more flexible

• Tables are made up of “fields” (columns) and “records” (rows)

• Queries are used to combine and subset tables into new tables

• Each table should have a unique, integer, ID, referred to as a primary key– Greatly improves query performance

Field Data Types

• Numeric– Float or integer– Auto numbered, use for primary keys

• Dates– YYYY-MM-DD HH:MM:SS.SS– 2013-04-05 14:23:12.34

• Text– Specified width– “Variant” width

• Binary Large Objects (BLOB)

What’s Wrong With This?Tree QueryLAT LON MEASYEAR MEASMON MEASDAY COMMON_NAME HT

45.446392 -122.236107 1995 6 22 Douglas-fir 4945.446392 -122.236107 1995 6 22 Douglas-fir 2745.446392 -122.236107 1995 6 22 Douglas-fir 9545.446392 -122.236107 1995 6 22 Douglas-fir 6645.446392 -122.236107 1995 6 22 Douglas-fir 11845.446392 -122.236107 1995 6 22 Douglas-fir 7645.446392 -122.236107 1995 6 22 Douglas-fir 14745.456116 -122.397774 1995 6 22 Douglas-fir 18545.456116 -122.397774 1995 6 22 Douglas-fir 10545.456116 -122.397774 1995 6 22 Douglas-fir 10545.456116 -122.397774 1995 6 22 Douglas-fir 8945.193054 -122.51667 1996 6 23 Douglas-fir 9045.193054 -122.51667 1996 6 23 Douglas-fir 9545.193054 -122.51667 1996 6 23 Douglas-fir 9645.193054 -122.51667 1996 6 23 Douglas-fir 99

Relational Databases

• Allow us to “relate” tables to:– Reduce the overall amount of data

• Removes duplicates

– Makes updates much easier– Improves search speeds

Entity-Relationship Diagram

• ERD– Unified Markup Language (UML)

Plot

TreeSpecies

Entities

Relationships

One to one

One to many

Many to many

Relationship Types

ID Lat Lon Year Month Day

1 45.446392 -122.236107 1995 6 22

2 45.193054 -122.51667 1995 6 22

Plot

ID Common Name

1 Douglas-fir

2 Ponderosa Pine

Species

ID PlotID SpeciesID Height

1 1 1 49

2 1 1 27

3 1 1 95

4 1 1 66

5 1 1 118

… 1 … …

12 2 1 90

13 2 1 95

Tree

Primary Key

Foreign Key

Database Normalization

1. Eliminate duplicate columns from the same table

2. Move fields that have “duplicate” row entries and move them to a related table

3. All field entries should be dependent on the primary key

4. There should be only one primary key in each table

Database Dictionary

• Defines each of the tables and fields in a database

• A database forms the basis for data management behind many GIS projects, web sites, and organizations

• Proper documentation is key to long term success!– Database design (including ERDs)– Database Dictionary

Geospatial Databases

• Not required to store spatial data!

• Provide:– Field types for spatial data: point, polyline,

polygon, etc.– Spatial operations: union, intersect, etc.– Spatial queries: return records that overlap

with a polygon, etc.– Some provide spatial reference control

What we really want

• What we need from a database:– Distributed, concurrent access

(concurrency)– Automatic Backup– Version control– Unlimited amounts of data– Quick data access– Inexpensive– Broad OS Support– File-level copying– GeoSpatial queries, operations, data types

Relational Databases

• Enterprise-Level – SQL Server– PostgreSQL– MySQL– Oracle– Sybase

• File-Level– Geodatabase– MS-Access

What we haveSQL Server PostgreSQL ESRI

GeodatabaseMS-Access

Concurrency Yes Yes No No

Automatic backup

Yes Yes No No

Versioning No No No No

Data Size 100s of millions 100s of millions 100,000? 100,000?

Performance Fast Fast Good Poor

Cost $600 per CPU Free ~$10,000 w/ArcGIS

~$400

OS Windows Any Windows Windows

File-level copy No No Yes Yes

Spatial Queries Yes Yes Yes No

Spatial data types

Yes Yes Yes No

Spatial operations

Yes Yes Yes No

Structured Query Language (SQL)

• Comes from the database industry

• “INSERT”, “DELETE”, and “SELECT” rows in tables

• Very rich syntax

• Portions of “SELECT” grammar used heavily in ArcGIS:– Selecting attributes– Raster calculator– Geodatabases

Transaction SQL

• “SQL” is a subset of T-SQL

• T-SQL allows full management of a database:– Create & drop:

• Tables, fields/columns, relationships, indexes, views, etc.

– Administrative functions

• Varies some between databases

Using SQL

• All Databases have “query editors” that allow us to write, save, edit, and use SQL queries

• Use programming languages to “write” queries and “fetch” records from the database

SQL: SELECT

SELECT Field1, Field2

FROM TableName INNER JOIN TableName2

ON TableName2.FK=TableName.PK

WHERE Filter1 AND Filter 2

GROUP BY Field1,Field2

ORDER BY Field1 [DESC], Field2 [DESC]

FK=Foreign Key, PK=Primary Key

Selecting Fields

• SELECT *– Returns all fields as new table

• SELECT Field1,Field2

• SELECT Table1.Field1,Table2.Field1– Return specified fields

• SELECT Table1.Field1 AS NewName– Avoids name collisions

Selecting Tables

• FROM Table1– Returns contents of one table

• FROM Table1 INNER JOIN Table2 ON Table2.ForeignKey=Table1.PrimaryKey– Returns records from Table2 that match

primary keys in Table1– Does not return all rows in Table1

Selecting Tables (con’t)

• FROM Table1 OUTER JOIN Table2 ON Table2.ForeignKey=Table1.PrimaryKey– Returns all matches between Table1 and

Table2 and any records in Table1 that don’t match records in Table2

– Missing values are NULL

Filters or “WHERE” clauses

SELECT *

FROM Table1

WHERE (Field1 Operator Value1) BooleanOperator (Field1 Operator Field2)

Filter Examples

• WHERE:– ID = 1– Area < 10000– Area <= 10000– Name = “Crater Lake” (case dependent)– Name LIKE “Crater Lake” (ignores case)

• Notice:– String values have double quotes– Syntax for strings vary some between

databases

SQL Comparisons

• Equals: =

• Greater than: >

• Less than: <

• Greater than or equal: >=

• Less than or equal: <=

• Not equal: <>

• Like: case independent string comparison with wild cards (%)

Boolean Operators

A B A AND B A OR B NOT A NOT B

T T T T F F

T F F T F T

F T F T T F

F F F F T T

More Complex Filter Examples

• WHERE:– Name LIKE “Hawaii” AND Area < 10000– Species LIKE “Ponderosa” AND DBH > 1

ORDER BY

SELECT *

FROM Table 1

ORDER BY LastName DESC, FirstName DESC

•Careful with performance on large datasets and string fields

GROUP BY

• Aggregates data

SELECT Species ,AVG(Height)

FROM Trees

GROUP BY Species

• Only aggregated fields can appear in SELECT list

SQL INSERT

• INSERT INTO TableName (Field1,Field2) VALUES (Value1,”Value2”)

• String values must be in quotes– Other values can also be in quotes

• If the table has an “auto numbered” ID field, it will be added automatically

• Otherwise, very difficult to set the ID field

SQL DELETEDELETE FROM TableName

WHERE ID=Value- Deletes one row

DELETE FROM Plot

WHERE PlotID=12

- Deletes all rows with PlotID=12

DELETE FROM TableName- Deletes everything in TableName!

Database Performance

Default Search

Indexed Search

Primary Key Search

Indexes

• Added to a table– Typically for one field

• Adds overhead to INSERT and DELETEs

• Important for:– Large tables– Complex queries– Especially text searches!

Maintaining Performance

• Always use integer, auto numbered primary keys

• Avoid iterative or hierarchical queries

• Sometimes code is faster:– Do simple query, load into RAM and sort

• With REALLY big data, don’t use SQL– NoSQL, accessing data directly, without the

use of a relational database package– There are “NoSQL” products in the works

• Avoid text searches and sorts

Rasters and Databases

• Don’t put rasters into a database!– Makes it impossible to backup and restore

the database– Put a file path to the rasters in the database