Inserting row into table and then updating it in sum of each column using ArcPy?

Inserting row into table and then updating it in sum of each column using ArcPy?

My problem is the following:

  • I have a table with an unknown number of columns (n)

  • Use arcpy.ListFields create a list of columns and generates Field name list

  • Then, using arcpy.da.InsertCursor create a new row at the end of the table…

for example

fieldNameList = [] fields = arcpy.ListFields(fc, "*") for field in fields: if field.type in ("Double", "Integer", "Single"): fieldNameList.append( cur = arcpy.da.InsertCursor(fc, fieldNameList) for x in xrange(-1, 0): cur.insertRow((SUM_FIELD1, SUM_FIELD2… SUM_FIELD n )) ?????????!!!! del cur

I do not know how to calculate the sum for each column and then the result to update in the created row. The sum should be separately calculated for each column…

I don't use Dictionaries (I don't fully understand them), however the new row can also be a list. Start by compiling a list of the sums and then insert that.

fieldNameList = [] values = [] # store the sum values here. fields = arcpy.ListFields(fc, "*") # get the OID/FID field name to skip desc = arcpy.Describe(fc) if desc.hasOID: OIDname = desc.OIDFieldName.upper() else: OIDname = "" for field in fields: if != OIDname: # skip the OID/FID field. if field.type in ("Double", "Integer", "Single"): # sum each suitable field, but not the NULL ones - they would be bad with arcpy.da.SearchCursor(fc,, + " is not NULL") as sCur: thisValue = 0 for row in sCur: thisValue += row[0] values.append(thisValue) # this will be the inserted row fieldNameList.append( with arcpy.da.InsertCursor(fc, fieldNameList) as cur: cur.insertRow(values)

This does of course mean that you'll read through the rows for each numeric field… but that also includes OID/FID - that's bad! the values for OID/FID can't be modified so best to skip that one. Also shape_area, shape_length are read-only but this looks like tabular data so I wont worry about that (this time).

For SQL Server, how to fix simultaneous parallel table updates?

I have to update all records (add Guids) on two (indexed) empty columns of 150 tables, each table with around 50k records (using a script to create 40k updates at once in c# and post it to the server) and exactly 4 existing columns.

On my local machine (16GB RAM, 500GB Samsung 850, SQL Server 2014, core i5) when I try to run 10 tables in parallel it takes a total of 13 minutes, while if I run 5 the process finishes in mere 1.7 minutes.

I do understand that something is busy on the disk level, but I need some help in how to quantify this huge difference in timings.

Is there a exact SQL Server DB view that I can check this discrepancy? Is there an exact way to figure out for a given hardware how many table updates can I run in parallel?? (the real test server has more RAM and 10k rpm disks).

Can anyone point to something that I can improve on the SQL Server to improve the timings for the running 10 tables in parallel?

I already tried increasing the Auto Growth size to 100MB from 10MB which improves the Disk Queue length (from around 5 to 0.1) but it does not actually decrease the total time that much.

I have asked the exact same question on stackoverflow, but not getting any helpful answers so far, so some or any insight/help would be immensely helpful. :)

Capturing Insert and Update Counts from Merge

This post shows hows how you can capture and store the number of records inserted, updated or deleted from a T-SQL Merge statement.

This is in response to a question on an earlier post about using Merge to load SCDs in a Data Warehouse.

You can achieve this by using the OUTPUT clause of a merge statement, including the $Action column that OUTPUT returns.

You wrap the Merge statement up as a sub-query, adding the OUTPUT clause to return details about what happened in the merge. Note that you can’t just select from this sub-query, there has to be an INSERT INTO statement.

One row will be returned for each row touched by the merge process.

The $action column will contain either INSERT, UPDATE or DELETE, to indicate what happened to that row.

You can also include Source.* in order to include the source column values in the output dataset.

You can also include DELETED.*, which returns the values of any updated records before they were updated, and INSERTED.* to show the values after the updated. In reality the records are not deleted or inserted, just updated, but DELETED/INSERTED is used as the terminology for old/new values either side of the update. When inserting a new record, DELETED values will be NULL.

You can then refer to this ‘MergeOutput’ result set at the top of the query by selecting from this sub-query.

There is a limitation, though, you can’t aggregate the table. So if we want to summarise the actions into a single row of insert, update and delete counts, we have to use a temporary table such as in the sample code below.

Step 4 - Create a Self Signed SQL Server Certificate:

The next step is to create a self-signed certificate that is protected by the database master key. A certificate is a digitally signed security object that contains a public (and optionally a private) key for SQL Server. An optional argument when creating a certificate is ENCRYPTION BY PASSWORD. This argument defines a password protection method of the certificate's private key. In our creation of the certificate we have chosen to not include this argument by doing so we are specifying that the certificate is to be protected by the database master key. Read more on about SQL Server certificates.

You use a DML MERGE statement to combine INSERT , UPDATE , and DELETE operations for a partitioned table into one statement and perform them atomically.

Pruning partitions when using a MERGE statement

When you run a MERGE statement against a partitioned table, you can limit the partitions involved in the statement by using the _PARTITIONTIME pseudo column (for ingestion-time partitioned tables) or by using the date, timestamp, or datetime column (for partitioned tables). Pruning partitions reduces cost and improves query performance.

You can use partition pruning conditions in the following places: in a subquery filter, a search_condition filter, or a merge_condition filter.

Each of the examples below queries an ingestion-time partitioned table using the _PARTITIONTIME pseudo column.

Using a subquery to filter source data

You can use a filter in a subquery to prune partitions. For example, in the following MERGE statement, only the rows in the 񟭒-01-01' partition in the source table are scanned.

Using a filter in the search_condition of a when_clause

The query optimizer attempts to use a filter in a search_condition to prune partitions. For example, in the following MERGE statement, only the rows in the following partitions are scanned in the target table: 񟭒-01-01' , 񟭒-01-02' , and 񟭒-01-03' .

In the following example, the WHEN NOT MATCHED BY SOURCE clause needs all data from the target table. As a result, all partitions are scanned, and you are charged for the bytes read in all partitions.

In general, when you use the WHEN NOT MATCHED and WHEN NOT MATCHED BY SOURCE clauses together, BigQuery assumes a FULL OUTER JOIN between the source and target tables. Normally, partitions cannot be pruned in a FULL OUTER JOIN . However, if a constant false predicate is used, the filter condition can be used for partition pruning. The following query uses partition pruning to scan only the 񟭒-01-01' partition in both the target and source tables.

Using a filter in a merge_condition

The query optimizer attempts to use a filter in a merge_condition to prune partitions. For example, following query will only scan the 񟭒-01-01' partition in both the target and source tables.

In this example, the merge_condition is used as a predicate to join the source and target tables. The query optimizer may or may not be able to use predicate push down (depending on the type of join).

In the following example, the MERGE statement does not allow partition pruning because the partition filter is a predicate in a join condition that cannot be applied directly on the table.

Benefits of using WITH TABLOCK on an INSERT

Under some circumstances, doing an INSERT INTO <tablename> (WITH TABLOCK) will be faster due to minimal logging. Those circumstances include having the database in the BULK_LOGGED recovery model.

Is there any other potential performance benefit to using WITH TABLOCK on an INSERT on an empty table when the database (tempdb) is using the SIMPLE recovery model?

I'm working with SQL Server 2012 Standard Edition.

My use case is for creating and then immediately populating a temp table within a stored procedure using an INSERT. SELECT , which could contain as many as a few million rows. I try to avoid that kind of tempdb abuse, but it is sometimes needed.

I'm trying to build a case to require TABLOCK . It doesn't seem like it would hurt anything, and might have a benefit. I'm trying to figure out if there is enough potential benefit to add it wherever throughout our code base, where I'm sure there is no other process that wants to write to the table.

I'm usually inserting into a newly created local temp table with a clustered PK, but do sometimes use a heap.

Inserting row into table and then updating it in sum of each column using ArcPy? - Geographic Information Systems

This chapter provides an introduction to schema objects and discusses tables, which are the most common types of schema objects.

This chapter contains the following sections:

Introduction to Schema Objects

A database schema is a logical container for data structures, called schema objects. Examples of schema objects are tables and indexes. You create and manipulate schema objects with SQL.

A database user account has a password and specific database privileges. Each user account owns a single schema, which has the same name as the user. The schema contains the data for the user owning the schema. For example, the hr user account owns the hr schema, which contains schema objects such as the employees table. In a production database, the schema owner usually represents a database application rather than a person.

Within a schema, each schema object of a particular type has a unique name. For example, hr.employees refers to the table employees in the hr schema. Figure 2-1 depicts a schema owner named hr and schema objects within the hr schema.

This section contains the following topics:

"Overview of Database Security" to learn more about users and privileges

Schema Object Types

Oracle SQL enables you to create and manipulate many other types of schema objects.

The principal types of schema objects are shown in the following table.

A table stores data in rows. Tables are the most important schema objects in a relational database.

Indexes are schema objects that contain an entry for each indexed row of the table or table cluster and provide direct, fast access to rows. Oracle Database supports several types of index. An index-organized table is a table in which the data is stored in an index structure.

Partitions are pieces of large tables and indexes. Each partition has its own name and may optionally have its own storage characteristics.

Views are customized presentations of data in one or more tables or other views. You can think of them as stored queries. Views do not actually contain data.

A sequence is a user-created object that can be shared by multiple users to generate integers. Typically, you use sequences to generate primary key values.

A dimension defines a parent-child relationship between pairs of column sets, where all the columns of a column set must come from the same table. Dimensions are commonly used to categorize data such as customers, products, and time.

A synonym is an alias for another schema object. Because a synonym is simply an alias, it requires no storage other than its definition in the data dictionary .

PL/SQL subprograms and packages

PL/SQL is the Oracle procedural extension of SQL. A PL/SQL subprogram is a named PL/SQL block that can be invoked with a set of parameters. A PL/SQL package groups logically related PL/SQL types, variables, and subprograms.

Other types of objects are also stored in the database and can be created and manipulated with SQL statements but are not contained in a schema. These objects include database user account, roles, contexts, and dictionary objects.

Oracle Database Administrator’s Guide to learn how to manage schema objects

Oracle Database SQL Language Reference for more about schema objects and database objects

Schema Object Storage

Some schema objects store data in a type of logical storage structure called a segment . For example, a nonpartitioned heap-organized table or an index creates a segment.

Other schema objects, such as views and sequences, consist of metadata only. This topic describes only schema objects that have segments.

Oracle Database stores a schema object logically within a tablespace . There is no relationship between schemas and tablespaces: a tablespace can contain objects from different schemas, and the objects for a schema can be contained in different tablespaces. The data of each object is physically contained in one or more data files.

The following figure shows a possible configuration of table and index segments, tablespaces, and data files. The data segment for one table spans two data files, which are both part of the same tablespace. A segment cannot span multiple tablespaces.

Figure 2-2 Segments, Tablespaces, and Data Files

"Logical Storage Structures" to learn about tablespaces and segments

Oracle Database Administrator’s Guide to learn how to manage storage for schema objects

Schema Object Dependencies

Some schema objects refer to other objects, creating a schema object dependency .

For example, a view contains a query that references tables or views, while a PL/SQL subprogram invokes other subprograms. If the definition of object A references object B, then A is a dependent object on B, and B is a referenced object for A.

Oracle Database provides an automatic mechanism to ensure that a dependent object is always up to date with respect to its referenced objects. When you create a dependent object, the database tracks dependencies between the dependent object and its referenced objects. When a referenced object changes in a way that might affect a dependent object, the database marks the dependent object invalid. For example, if a user drops a table, no view based on the dropped table is usable.

An invalid dependent object must be recompiled against the new definition of a referenced object before the dependent object is usable. Recompilation occurs automatically when the invalid dependent object is referenced.

As an illustration of how schema objects can create dependencies, the following sample script creates a table test_table and then a procedure that queries this table:

The following query of the status of procedure test_proc shows that it is valid:

After adding the col3 column to test_table , the procedure is still valid because the procedure has no dependencies on this column:

However, changing the data type of the col1 column, which the test_proc procedure depends on in, invalidates the procedure:

Running or recompiling the procedure makes it valid again, as shown in the following example:

SYS and SYSTEM Schemas

All Oracle databases include default administrative accounts.

Administrative accounts are highly privileged and are intended only for DBAs authorized to perform tasks such as starting and stopping the database, managing memory and storage, creating and managing database users, and so on.

The SYS administrative account is automatically created when a database is created. This account can perform all database administrative functions. The SYS schema stores the base tables and views for the data dictionary . These base tables and views are critical for the operation of Oracle Database. Tables in the SYS schema are manipulated only by the database and must never be modified by any user.

The SYSTEM administrative account is also automatically created when a database is created. The SYSTEM schema stores additional tables and views that display administrative information, and internal tables and views used by various Oracle Database options and tools. Never use the SYSTEM schema to store tables of interest to nonadministrative users.

Oracle Database Administrator’s Guide to learn about SYS , SYSTEM , and other administrative accounts

Sample Schemas

An Oracle database may include sample schemas, which are a set of interlinked schemas that enable Oracle documentation and Oracle instructional materials to illustrate common database tasks.

The hr sample schema contains information about employees, departments and locations, work histories, and so on. The following illustration depicts an entity-relationship diagram of the tables in hr . Most examples in this manual use objects from this schema.

Oracle Database Sample Schemas to learn how to install the sample schemas

Overview of Tables

A table is the basic unit of data organization in an Oracle database.

A table describes an entity , which is something of significance about which information must be recorded. For example, an employee could be an entity.

Oracle Database tables fall into the following basic categories:

Relational tables have simple columns and are the most common table type. Example 2-1 shows a CREATE TABLE statement for a relational table.

The columns correspond to the top-level attributes of an object type . See "Overview of Object Tables" .

You can create a relational table with the following organizational characteristics:

A heap-organized table does not store rows in any particular order. The CREATE TABLE statement creates a heap-organized table by default.

An index-organized table orders rows according to the primary key values. For some applications, index-organized tables enhance performance and use disk space more efficiently. See "Overview of Index-Organized Tables" .

An external table is a read-only table whose metadata is stored in the database but whose data is stored outside the database. See "Overview of External Tables" .

A table is either permanent or temporary. A permanent table definition and data persist across sessions. A temporary table definition persists in the same way as a permanent table definition, but the data exists only for the duration of a transaction or session . Temporary tables are useful in applications where a result set must be held temporarily, perhaps because the result is constructed by running multiple operations.

This topic contains the following topics:


A table definition includes a table name and set of columns.

A column identifies an attribute of the entity described by the table. For example, the column employee_id in the employees table refers to the employee ID attribute of an employee entity.

In general, you give each column a column name, a data type , and a width when you create a table. For example, the data type for employee_id is NUMBER(6) , indicating that this column can only contain numeric data up to 6 digits in width. The width can be predetermined by the data type, as with DATE .

Virtual Columns

A table can contain a virtual column , which unlike a nonvirtual column does not consume disk space.

The database derives the values in a virtual column on demand by computing a set of user-specified expressions or functions. For example, the virtual column income could be a function of the salary and commission_pct columns.

Oracle Database Administrator’s Guide to learn how to manage virtual columns

Invisible Columns

An invisible column is a user-specified column whose values are only visible when the column is explicitly specified by name. You can add an invisible column to a table without affecting existing applications, and make the column visible if necessary.

In general, invisible columns help migrate and evolve online applications. A use case might be an application that queries a three-column table with a SELECT * statement. Adding a fourth column to the table would break the application, which expects three columns of data. Adding a fourth invisible column makes the application function normally. A developer can then alter the application to handle a fourth column, and make the column visible when the application goes live.

The following example creates a table products with an invisible column count , and then makes the invisible column visible:

Oracle Database Administrator’s Guide to learn how to manage invisible columns

Oracle Database SQL Language Reference for more information about invisible columns

A row is a collection of column information corresponding to a record in a table.

For example, a row in the employees table describes the attributes of a specific employee: employee ID, last name, first name, and so on. After you create a table, you can insert, query, delete, and update rows using SQL.

Example: CREATE TABLE and ALTER TABLE Statements

The Oracle SQL statement to create a table is CREATE TABLE .

Example 2-1 CREATE TABLE employees

The following example shows the CREATE TABLE statement for the employees table in the hr sample schema. The statement specifies columns such as employee_id , first_name , and so on, specifying a data type such as NUMBER or DATE for each column.

Example 2-2 ALTER TABLE employees

The following example shows an ALTER TABLE statement that adds integrity constraints to the employees table. Integrity constraints enforce business rules and prevent the entry of invalid information into tables.

Example 2-3 Rows in the employees Table

The following sample output shows 8 rows and 6 columns of the hr.employees table.

The preceding output illustrates some of the following important characteristics of tables, columns, and rows:

A row of the table describes the attributes of one employee: name, salary, department, and so on. For example, the first row in the output shows the record for the employee named Steven King.

A column describes an attribute of the employee. In the example, the employee_id column is the primary key , which means that every employee is uniquely identified by employee ID. Any two employees are guaranteed not to have the same employee ID.

A non-key column can contain rows with identical values. In the example, the salary value for employees 101 and 102 is the same: 17000 .

A foreign key column refers to a primary or unique key in the same table or a different table. In this example, the value of 90 in department_id corresponds to the department_id column of the departments table.

A field is the intersection of a row and column. It can contain only one value. For example, the field for the department ID of employee 103 contains the value 60 .

A field can lack a value. In this case, the field is said to contain a null value. The value of the commission_pct column for employee 100 is null, whereas the value in the field for employee 149 is .2 . A column allows nulls unless a NOT NULL or primary key integrity constraint has been defined on this column, in which case no row can be inserted without a value for this column.

Oracle Database SQL Language Reference for CREATE TABLE syntax and semantics

Oracle Data Types

Each column has a data type , which is associated with a specific storage format, constraints, and valid range of values. The data type of a value associates a fixed set of properties with the value.

These properties cause Oracle Database to treat values of one data type differently from values of another. For example, you can multiply values of the NUMBER data type, but not values of the RAW data type.

When you create a table, you must specify a data type for each of its columns. Each value subsequently inserted in a column assumes the column data type.

Oracle Database provides several built-in data types. The most commonly used data types fall into the following categories:

Other important categories of built-in types include raw, large objects (LOBs), and collections. PL/SQL has data types for constants and variables, which include BOOLEAN , reference types, composite types (records), and user-defined types.

Oracle Database SQL Language Reference to learn about built-in SQL data types

Oracle Database Development Guide to learn how to use the built-in data types

Character Data Types

Character data types store alphanumeric data in strings. The most common character data type is VARCHAR2 , which is the most efficient option for storing character data.

The byte values correspond to the character encoding scheme, generally called a character set . The database character set is established at database creation. Examples of character sets are 7-bit ASCII, EBCDIC, and Unicode UTF-8.

The length semantics of character data types are measurable in bytes or characters. The treatment of strings as a sequence of bytes is called byte semantics . This is the default for character data types. The treatment of strings as a sequence of characters is called character semantics . A character is a code point of the database character set.

Oracle Database 2 Day Developer's Guide for a brief introduction to data types

Oracle Database Development Guide to learn how to choose a character data type

VARCHAR2 and CHAR Data Types

For example, 'LILA' , 'St. George Island' , and '101' are all character literals 5001 is a numeric literal. Character literals are enclosed in single quotation marks so that the database can distinguish them from schema object names.

This manual uses the terms text literal , character literal , and string interchangeably.

When you create a table with a VARCHAR2 column, you specify a maximum string length. In Example 2-1, the last_name column has a data type of VARCHAR2(25) , which means that any name stored in the column has a maximum of 25 bytes.

For each row, Oracle Database stores each value in the column as a variable-length field unless a value exceeds the maximum length, in which case the database returns an error. For example, in a single-byte character set, if you enter 10 characters for the last_name column value in a row, then the column in the row piece stores only 10 characters (10 bytes), not 25. Using VARCHAR2 reduces space consumption.

In contrast to VARCHAR2 , CHAR stores fixed-length character strings. When you create a table with a CHAR column, the column requires a string length. The default is 1 byte. The database uses blanks to pad the value to the specified length.

Oracle Database compares VARCHAR2 values using nonpadded comparison semantics and compares CHAR values using blank-padded comparison semantics.

Oracle Database SQL Language Reference for details about blank-padded and nonpadded comparison semantics

NCHAR and NVARCHAR2 Data Types

Unicode is a universal encoded character set that can store information in any language using a single character set. NCHAR stores fixed-length character strings that correspond to the national character set, whereas NVARCHAR2 stores variable length character strings.

You specify a national character set when creating a database. The character set of NCHAR and NVARCHAR2 data types must be either AL16UTF16 or UTF8 . Both character sets use Unicode encoding.

When you create a table with an NCHAR or NVARCHAR2 column, the maximum size is always in character length semantics. Character length semantics is the default and only length semantics for NCHAR or NVARCHAR2 .

Oracle Database Globalization Support Guide for information about Oracle's globalization support feature

Numeric Data Types

The Oracle Database numeric data types store fixed and floating-point numbers, zero, and infinity. Some numeric types also store values that are the undefined result of an operation, which is known as "not a number" or NaN .

Oracle Database stores numeric data in variable-length format. Each value is stored in scientific notation, with 1 byte used to store the exponent. The database uses up to 20 bytes to store the mantissa , which is the part of a floating-point number that contains its significant digits. Oracle Database does not store leading and trailing zeros.

NUMBER Data Type

The NUMBER data type stores fixed and floating-point numbers. The database can store numbers of virtually any magnitude. This data is guaranteed to be portable among different operating systems running Oracle Database. The NUMBER data type is recommended for most cases in which you must store numeric data.

You specify a fixed-point number in the form NUMBER ( p , s ) , where p and s refer to the following characteristics:

The precision specifies the total number of digits. If a precision is not specified, then the column stores the values exactly as provided by the application without any rounding.

The scale specifies the number of digits from the decimal point to the least significant digit. Positive scale counts digits to the right of the decimal point up to and including the least significant digit. Negative scale counts digits to the left of the decimal point up to but not including the least significant digit. If you specify a precision without a scale, as in NUMBER(6) , then the scale is 0.

In Example 2-1, the salary column is type NUMBER(8,2) , so the precision is 8 and the scale is 2. Thus, the database stores a salary of 100,000 as 100000.00 .

Floating-Point Numbers

Oracle Database provides two numeric data types exclusively for floating-point numbers: BINARY_FLOAT and BINARY_DOUBLE .

These types support all of the basic functionality provided by the NUMBER data type. However, whereas NUMBER uses decimal precision, BINARY_FLOAT and BINARY_DOUBLE use binary precision, which enables faster arithmetic calculations and usually reduces storage requirements.

BINARY_FLOAT and BINARY_DOUBLE are approximate numeric data types. They store approximate representations of decimal values, rather than exact representations. For example, the value 0.1 cannot be exactly represented by either BINARY_DOUBLE or BINARY_FLOAT . They are frequently used for scientific computations. Their behavior is similar to the data types FLOAT and DOUBLE in Java and XMLSchema.

Oracle Database SQL Language Reference to learn about precision, scale, and other characteristics of numeric types

Datetime Data Types

The datetime data types are DATE and TIMESTAMP . Oracle Database provides comprehensive time zone support for time stamps.

DATE Data Type

The DATE data type stores date and time. Although datetimes can be represented in character or number data types, DATE has special associated properties.

The database stores dates internally as numbers. Dates are stored in fixed-length fields of 7 bytes each, corresponding to century, year, month, day, hour, minute, and second.

Dates fully support arithmetic operations, so you add to and subtract from dates just as you can with numbers.

The database displays dates according to the specified format model . A format model is a character literal that describes the format of a datetime in a character string. The standard date format is DD-MON-RR , which displays dates in the form 01-JAN-11 .

RR is similar to YY (the last two digits of the year), but the century of the return value varies according to the specified two-digit year and the last two digits of the current year. Assume that in 1999 the database displays 01-JAN-11 . If the date format uses RR , then 11 specifies 2011 , whereas if the format uses YY , then 11 specifies 1911 . You can change the default date format at both the database instance and session level.

Oracle Database stores time in 24-hour format— HH:MI:SS . If no time portion is entered, then by default the time in a date field is 00:00:00 A.M . In a time-only entry, the date portion defaults to the first day of the current month.

Oracle Database Development Guide for more information about centuries and date format masks

Oracle Database SQL Language Reference for information about datetime format codes

Oracle Database Development Guide to learn how to perform arithmetic operations with datetime data types


The TIMESTAMP data type is an extension of the DATE data type.

TIMESTAMP stores fractional seconds in addition to the information stored in the DATE data type. The TIMESTAMP data type is useful for storing precise time values, such as in applications that must track event order.

The DATETIME data types TIMESTAMP WITH TIME ZONE and TIMESTAMP WITH LOCAL TIME ZONE are time-zone aware. When a user selects the data, the value is adjusted to the time zone of the user session. This data type is useful for collecting and evaluating date information across geographic regions.

Oracle Database SQL Language Reference for details about the syntax of creating and entering data in time stamp columns

Rowid Data Types

Every row stored in the database has an address. Oracle Database uses a ROWID data type to store the address (rowid) of every row in the database.

Rowids fall into the following categories:

Physical rowids store the addresses of rows in heap-organized tables, table clusters, and table and index partitions.

Logical rowids store the addresses of rows in index-organized tables.

Foreign rowids are identifiers in foreign tables, such as DB2 tables accessed through a gateway. They are not standard Oracle Database rowids.

A data type called the universal rowid , or urowid, supports all types of rowids.

Use of Rowids

A B-tree index , which is the most common type, contains an ordered list of keys divided into ranges. Each key is associated with a rowid that points to the associated row's address for fast access.

End users and application developers can also use rowids for several important functions:

Rowids are the fastest means of accessing particular rows.

Rowids provide the ability to see how a table is organized.

Rowids are unique identifiers for rows in a given table.

You can also create tables with columns defined using the ROWID data type. For example, you can define an exception table with a column of data type ROWID to store the rowids of rows that violate integrity constraints. Columns defined using the ROWID data type behave like other table columns: values can be updated, and so on.

ROWID Pseudocolumn

Every table in an Oracle database has a pseudocolumn named ROWID .

A pseudocolumn behaves like a table column, but is not actually stored in the table. You can select from pseudocolumns, but you cannot insert, update, or delete their values. A pseudocolumn is also similar to a SQL function without arguments. Functions without arguments typically return the same value for every row in the result set, whereas pseudocolumns typically return a different value for each row.

Values of the ROWID pseudocolumn are strings representing the address of each row. These strings have the data type ROWID . This pseudocolumn is not evident when listing the structure of a table by executing SELECT or DESCRIBE , nor does the pseudocolumn consume space. However, the rowid of each row can be retrieved with a SQL query using the reserved word ROWID as a column name.

The following example queries the ROWID pseudocolumn to show the rowid of the row in the employees table for employee 100:

Oracle Database Development Guide to learn how to identify rows by address

Format Models and Data Types

A format model is a character literal that describes the format of datetime or numeric data stored in a character string. A format model does not change the internal representation of the value in the database.

When you convert a character string into a date or number, a format model determines how the database interprets the string. In SQL, you can use a format model as an argument of the TO_CHAR and TO_DATE functions to format a value to be returned from the database or to format a value to be stored in the database.

The following statement selects the salaries of the employees in Department 80 and uses the TO_CHAR function to convert these salaries into character values with the format specified by the number format model '$99,990.99' :

The following example updates a hire date using the TO_DATE function with the format mask 'YYYY MM DD' to convert the string '1998 05 20' to a DATE value:

Integrity Constraints

An integrity constraint is a named rule that restrict the values for one or more columns in a table.

Data integrity rules prevent invalid data entry into tables. Also, constraints can prevent the deletion of a table when certain dependencies exist.

If a constraint is enabled, then the database checks data as it is entered or updated. Oracle Database prevents data that does not conform to the constraint from being entered. If a constraint is disabled, then Oracle Database allows data that does not conform to the constraint to enter the database.

In Example 2-1, the CREATE TABLE statement specifies NOT NULL constraints for the last_name , email , hire_date , and job_id columns. The constraint clauses identify the columns and the conditions of the constraint. These constraints ensure that the specified columns contain no null values. For example, an attempt to insert a new employee without a job ID generates an error.

You can create a constraint when or after you create a table. You can temporarily disable constraints if needed. The database stores constraints in the data dictionary .

"Data Integrity" to learn about integrity constraints

"Overview of the Data Dictionary" to learn about the data dictionary

Oracle Database SQL Language Reference to learn about SQL constraint clauses

Table Storage

Oracle Database uses a data segment in a tablespace to hold table data.

A segment contains extents made up of data blocks. The data segment for a table (or cluster data segment, for a table cluster ) is located in either the default tablespace of the table owner or in a tablespace named in the CREATE TABLE statement.

"User Segments" to learn about the types of segments and how they are created

Table Organization

By default, a table is organized as a heap, which means that the database places rows where they fit best rather than in a user-specified order. Thus, a heap-organized table is an unordered collection of rows.

Index-organized tables use a different principle of organization.

As users add rows, the database places the rows in the first available free space in the data segment. Rows are not guaranteed to be retrieved in the order in which they were inserted.

The hr.departments table is a heap-organized table. It has columns for department ID, name, manager ID, and location ID. As rows are inserted, the database stores them wherever they fit. A data block in the table segment might contain the unordered rows shown in the following example:

The column order is the same for all rows in a table. The database usually stores columns in the order in which they were listed in the CREATE TABLE statement, but this order is not guaranteed. For example, if a table has a column of type LONG , then Oracle Database always stores this column last in the row. Also, if you add a new column to a table, then the new column becomes the last column stored.

A table can contain a virtual column , which unlike normal columns does not consume space on disk. The database derives the values in a virtual column on demand by computing a set of user-specified expressions or functions. You can index virtual columns, collect statistics on them, and create integrity constraints. Thus, virtual columns are much like nonvirtual columns.

Row Storage

The database stores rows in data blocks. Each row of a table containing data for less than 256 columns is contained in one or more row pieces.

If possible, Oracle Database stores each row as one row piece . However, if all of the row data cannot be inserted into a single data block, or if an update to an existing row causes the row to outgrow its data block, then the database stores the row using multiple row pieces.

Rows in a table cluster contain the same information as rows in nonclustered tables. Additionally, rows in a table cluster contain information that references the cluster key to which they belong.

"Data Block Format" to learn about the components of a data block

Rowids of Row Pieces

Every row in a heap-organized table has a rowid unique to this table that corresponds to the physical address of a row piece. For table clusters, rows in different tables that are in the same data block can have the same rowid.

Oracle Database uses rowids internally for the construction of indexes. For example, each key in a B-tree index is associated with a rowid that points to the address of the associated row for fast access. Physical rowids provide the fastest possible access to a table row, enabling the database to retrieve a row in as little as a single I/O.

"Rowid Format" to learn about the structure of a rowid

"Overview of B-Tree Indexes" to learn about the types and structure of B-tree indexes

Storage of Null Values

A null is the absence of a value in a column. Nulls indicate missing, unknown, or inapplicable data.

Nulls are stored in the database if they fall between columns with data values. In these cases, they require 1 byte to store the length of the column (zero). Trailing nulls in a row require no storage because a new row header signals that the remaining columns in the previous row are null. For example, if the last three columns of a table are null, then no data is stored for these columns.

Table Compression

The database can use table compression to reduce the amount of storage required for the table.

Compression saves disk space, reduces memory use in the database buffer cache, and in some cases speeds query execution. Table compression is transparent to database applications.

Basic Table Compression and Advanced Row Compression

Dictionary-based table compression provides good compression ratios for heap-organized tables.

Oracle Database supports the following types of dictionary-based table compression:

This type of compression is intended for bulk load operations. The database does not compress data modified using conventional DML. You must use direct path INSERT operations, ALTER TABLE . . . MOVE operations, or online table redefinition to achieve basic table compression.

This type of compression is intended for OLTP applications and compresses data manipulated by any SQL operation. The database achieves a competitive compression ratio while enabling the application to perform DML in approximately the same amount of time as DML on an uncompressed table.

For the preceding types of compression, the database stores compressed rows in row major format . All columns of one row are stored together, followed by all columns of the next row, and so on. The database replaces duplicate values with a short reference to a symbol table stored at the beginning of the block. Thus, information that the database needs to re-create the uncompressed data is stored in the data block itself.

Compressed data blocks look much like normal data blocks. Most database features and functions that work on regular data blocks also work on compressed blocks.

You can declare compression at the tablespace, table, partition, or subpartition level. If specified at the tablespace level, then all tables created in the tablespace are compressed by default.

Example 2-4 Table-Level Compression

The following statement applies advanced row compression to the orders table:

Example 2-5 Partition-Level Compression

The following example of a partial CREATE TABLE statement specifies advanced row compression for one partition and basic table compression for the other partition:

"Row Format" to learn how values are stored in a row

"Data Block Compression" to learn about the format of compressed data blocks

"SQL*Loader" to learn about using SQL*Loader for direct path loads

Hybrid Columnar Compression

With Hybrid Columnar Compression, the database stores the same column for a group of rows together. The data block does not store data in row-major format, but uses a combination of both row and columnar methods.

Storing column data together, with the same data type and similar characteristics, dramatically increases the storage savings achieved from compression. The database compresses data manipulated by any SQL operation, although compression levels are higher for direct path loads. Database operations work transparently against compressed objects, so no application changes are required.

Hybrid Column Compression and In-Memory Column Store (IM column store) are closely related. The primary difference is that Hybrid Column Compression optimizes disk storage, whereas the IM column store optimizes memory storage.

"In-Memory Area" to learn more about the IM column store

Types of Hybrid Columnar Compression

If your underlying storage supports Hybrid Columnar Compression, then you can specify different types of compression, depending on your requirements.

The compression options are:

This type of compression is optimized to save storage space, and is intended for data warehouse applications.

This type of compression is optimized for maximum compression levels, and is intended for historical data and data that does not change.

Hybrid Columnar Compression is optimized for data warehousing and decision support applications on Oracle Exadata storage. Oracle Exadata maximizes the performance of queries on tables that are compressed using Hybrid Columnar Compression, taking advantage of the processing power, memory, and Infiniband network bandwidth that are integral to the Oracle Exadata storage server.

Other Oracle storage systems support Hybrid Columnar Compression, and deliver the same space savings as on Oracle Exadata storage, but do not deliver the same level of query performance. For these storage systems, Hybrid Columnar Compression is ideal for in-database archiving of older data that is infrequently accessed.

Compression Units

Hybrid Columnar Compression uses a logical construct called a compression unit to store a set of rows.

When you load data into a table, the database stores groups of rows in columnar format, with the values for each column stored and compressed together. After the database has compressed the column data for a set of rows, the database fits the data into the compression unit.

For example, you apply Hybrid Columnar Compression to a daily_sales table. At the end of every day, you populate the table with items and the number sold, with the item ID and date forming a composite primary key. The following table shows a subset of the rows in daily_sales .

Table 2-2 Sample Table daily_sales

Assume that this subset of rows is stored in one compression unit. Hybrid Columnar Compression stores the values for each column together, and then uses multiple algorithms to compress each column. The database chooses the algorithms based on a variety of factors, including the data type of the column, the cardinality of the actual values in the column, and the compression level chosen by the user.

As shown in the following graphic, each compression unit can span multiple data blocks. The values for a particular column may or may not span multiple blocks.

Figure 2-4 Compression Unit

If Hybrid Columnar Compression does not lead to space savings, then the database stores the data in the DBMS_COMPRESSION.COMP_BLOCK format. In this case, the database applies OLTP compression to the blocks, which reside in a Hybrid Columnar Compression segment.

Oracle Database Licensing Information User Manual to learn about licensing requirements for Hybrid Columnar Compression

Oracle Database Administrator’s Guide to learn how to use Hybrid Columnar Compression

Oracle Database SQL Language Reference for CREATE TABLE syntax and semantics

DML and Hybrid Columnar Compression

Hybrid Columnar Compression has implications for row locking in different types of DML operations.

Direct Path Loads and Conventional Inserts

When loading data into a table that uses Hybrid Columnar Compression, you can use either conventional inserts or direct path loads. Direct path loads lock the entire table, which reduces concurrency.

Oracle Database 12c Release 2 (12.2) adds support for conventional array inserts into the Hybrid Columnar Compression format. The advantages of conventional array inserts are:

Inserted rows use row-level locks, which increases concurrency.

Automatic Data Optimization (ADO) and Heat Map support Hybrid Columnar Compression for row-level policies. Thus, the database can use Hybrid Columnar Compression for eligible blocks even when DML activity occurs on other parts of the segment.

When the application uses conventional array inserts, Oracle Database stores the rows in compression units when the following conditions are met:

The table is stored in an ASSM tablespace.

The compatibility level is or later.

The table definition satisfies the existing Hybrid Columnar Compression table constraints, including no columns of type LONG , and no row dependencies.

Conventional inserts generate redo and undo. Thus, compression units created by conventional DML statement are rolled back or committed along with the DML. The database automatically performs index maintenance, just as for rows that are stored in conventional data blocks.

By default, the database locks all rows in the compression unit if an update or delete is applied to any row in the unit. To avoid this issue, you can choose to enable row-level locking for a table. In this case, the database only locks rows that are affected by the update or delete operation.

Oracle Database Administrator’s Guide to learn how to perform conventional inserts

Overview of Table Clusters

A table cluster is a group of tables that share common columns and store related data in the same blocks.

When tables are clustered, a single data block can contain rows from multiple tables. For example, a block can store rows from both the employees and departments tables rather than from only a single table.

The cluster key is the column or columns that the clustered tables have in common. For example, the employees and departments tables share the department_id column. You specify the cluster key when creating the table cluster and when creating every table added to the table cluster.

The cluster key value is the value of the cluster key columns for a particular set of rows. All data that contains the same cluster key value, such as department_id=20 , is physically stored together. Each cluster key value is stored only once in the cluster and the cluster index, no matter how many rows of different tables contain the value.

For an analogy, suppose an HR manager has two book cases: one with boxes of employee folders and the other with boxes of department folders. Users often ask for the folders for all employees in a particular department. To make retrieval easier, the manager rearranges all the boxes in a single book case. She divides the boxes by department ID. Thus, all folders for employees in department 20 and the folder for department 20 itself are in one box the folders for employees in department 100 and the folder for department 100 are in another box, and so on.

Consider clustering tables when they are primarily queried (but not modified) and records from the tables are frequently queried together or joined. Because table clusters store related rows of different tables in the same data blocks, properly used table clusters offer the following benefits over nonclustered tables:

Disk I/O is reduced for joins of clustered tables.

Access time improves for joins of clustered tables.

Less storage is required to store related table and index data because the cluster key value is not stored repeatedly for each row.

Typically, clustering tables is not appropriate in the following situations:

The tables are frequently updated.

The tables frequently require a full table scan .

The tables require truncating.

Oracle Database SQL Tuning Guide for guidelines on when to use table clusters

Overview of Indexed Clusters

An index cluster is a table cluster that uses an index to locate data. The cluster index is a B-tree index on the cluster key. A cluster index must be created before any rows can be inserted into clustered tables.

Example 2-6 Creating a Table Cluster and Associated Index

Assume that you create the cluster employees_departments_cluster with the cluster key department_id , as shown in the following example:

Because the HASHKEYS clause is not specified, employees_departments_cluster is an indexed cluster. The preceding example creates an index named idx_emp_dept_cluster on the cluster key department_id .

Example 2-7 Creating Tables in an Indexed Cluster

You create the employees and departments tables in the cluster, specifying the department_id column as the cluster key, as follows (the ellipses mark the place where the column specification goes):

Assume that you add rows to the employees and departments tables. The database physically stores all rows for each department from the employees and departments tables in the same data blocks. The database stores the rows in a heap and locates them with the index.

Figure 2-5 shows the employees_departments_cluster table cluster, which contains employees and departments . The database stores rows for employees in department 20 together, department 110 together, and so on. If the tables are not clustered, then the database does not ensure that the related rows are stored together.

Figure 2-5 Clustered Table Data

The B-tree cluster index associates the cluster key value with the database block address (DBA) of the block containing the data. For example, the index entry for key 20 shows the address of the block that contains data for employees in department 20:

The cluster index is separately managed, just like an index on a nonclustered table, and can exist in a separate tablespace from the table cluster.

Oracle Database Administrator’s Guide to learn how to create and manage indexed clusters

Oracle Database SQL Language Reference for CREATE CLUSTER syntax and semantics

Overview of Hash Clusters

A hash cluster is like an indexed cluster, except the index key is replaced with a hash function . No separate cluster index exists. In a hash cluster, the data is the index.

With an indexed table or indexed cluster, Oracle Database locates table rows using key values stored in a separate index. To find or store a row in an indexed table or table cluster, the database must perform at least two I/Os:

One or more I/Os to find or store the key value in the index

Another I/O to read or write the row in the table or table cluster

To find or store a row in a hash cluster, Oracle Database applies the hash function to the cluster key value of the row. The resulting hash value corresponds to a data block in the cluster, which the database reads or writes on behalf of the issued statement.

Hashing is an optional way of storing table data to improve the performance of data retrieval. Hash clusters may be beneficial when the following conditions are met:

A table is queried much more often than modified.

The hash key column is queried frequently with equality conditions, for example, WHERE department_id=20 . For such queries, the cluster key value is hashed. The hash key value points directly to the disk area that stores the rows.

You can reasonably guess the number of hash keys and the size of the data stored with each key value.

Hash Cluster Creation

To create a hash cluster, you use the same CREATE CLUSTER statement as for an indexed cluster, with the addition of a hash key. The number of hash values for the cluster depends on the hash key.

The cluster key, like the key of an indexed cluster, is a single column or composite key shared by the tables in the cluster. A hash key value is an actual or possible value inserted into the cluster key column. For example, if the cluster key is department_id , then hash key values could be 10, 20, 30, and so on.

Oracle Database uses a hash function that accepts an infinite number of hash key values as input and sorts them into a finite number of buckets. Each bucket has a unique numeric ID known as a hash value . Each hash value maps to the database block address for the block that stores the rows corresponding to the hash key value (department 10, 20, 30, and so on).

In the following example, the number of departments that are likely to exist is 100, so HASHKEYS is set to 100 :

After you create employees_departments_cluster , you can create the employees and departments tables in the cluster. You can then load data into the hash cluster just as in the indexed cluster.

Oracle Database Administrator’s Guide to learn how to create and manage hash clusters

Hash Cluster Queries

In queries of a hash cluster, the database determines how to hash the key values input by the user.

For example, users frequently execute queries such as the following, entering different department ID numbers for p_id :

If a user queries employees in department_id =20 , then the database might hash this value to bucket 77. If a user queries employees in department_id = 10 , then the database might hash this value to bucket 15. The database uses the internally generated hash value to locate the block that contains the employee rows for the requested department.

The following illustration depicts a hash cluster segment as a horizontal row of blocks. As shown in the graphic, a query can retrieve data in a single I/O.

Figure 2-6 Retrieving Data from a Hash Cluster

A limitation of hash clusters is the unavailability of range scans on nonindexed cluster keys. Assume no separate index exists for the hash cluster created in Hash Cluster Creation. A query for departments with IDs between 20 and 100 cannot use the hashing algorithm because it cannot hash every possible value between 20 and 100. Because no index exists, the database must perform a full scan.

Hash Cluster Variations

A single-table hash cluster is an optimized version of a hash cluster that supports only one table at a time. A one-to-one mapping exists between hash keys and rows.

A single-table hash cluster can be beneficial when users require rapid access to a table by primary key. For example, users often look up an employee record in the employees table by employee_id .

A sorted hash cluster stores the rows corresponding to each value of the hash function in such a way that the database can efficiently return them in sorted order. The database performs the optimized sort internally. For applications that always consume data in sorted order, this technique can mean faster retrieval of data. For example, an application might always sort on the order_date column of the orders table.

Oracle Database Administrator’s Guide to learn how to create single-table and sorted hash clusters

Hash Cluster Storage

Oracle Database allocates space for a hash cluster differently from an indexed cluster.

In the example in Hash Cluster Creation, HASHKEYS specifies the number of departments likely to exist, whereas SIZE specifies the size of the data associated with each department. The database computes a storage space value based on the following formula:

Thus, if the block size is 4096 bytes in the example shown in Hash Cluster Creation, then the database allocates at least 200 blocks to the hash cluster.

Oracle Database does not limit the number of hash key values that you can insert into the cluster. For example, even though HASHKEYS is 100 , nothing prevents you from inserting 200 unique departments in the departments table. However, the efficiency of the hash cluster retrieval diminishes when the number of hash values exceeds the number of hash keys.

To illustrate the retrieval issues, assume that block 100 in Figure 2-6 is completely full with rows for department 20. A user inserts a new department with department_id 43 into the departments table. The number of departments exceeds the HASHKEYS value, so the database hashes department_id 43 to hash value 77, which is the same hash value used for department_id 20. Hashing multiple input values to the same output value is called a hash collision .

When users insert rows into the cluster for department 43, the database cannot store these rows in block 100, which is full. The database links block 100 to a new overflow block, say block 200, and stores the inserted rows in the new block. Both block 100 and 200 are now eligible to store data for either department. As shown in Figure 2-7, a query of either department 20 or 43 now requires two I/Os to retrieve the data: block 100 and its associated block 200. You can solve this problem by re-creating the cluster with a different HASHKEYS value.

Figure 2-7 Retrieving Data from a Hash Cluster When a Hash Collision Occurs

Oracle Database Administrator’s Guide to learn how to manage space in hash clusters

Overview of Attribute-Clustered Tables

An attribute-clustered table is a heap-organized table that stores data in close proximity on disk based on user-specified clustering directives. The directives specify columns in single or multiple tables.

The directives are as follows:

The CLUSTERING . BY LINEAR ORDER directive orders data in a table according to specified columns.

Consider using BY LINEAR ORDER clustering, which is the default, when queries qualify the prefix of columns specified in the clustering clause. For example, if queries of sh.sales often specify either a customer ID or both customer ID and product ID, then you could cluster data in the table using the linear column order cust_id , prod_id .

The CLUSTERING . BY INTERLEAVED ORDER directive orders data in one or more tables using a special algorithm, similar to a Z-order function, that permits multicolumn I/O reduction.

Consider using BY INTERLEAVED ORDER clustering when queries specify a variety of column combinations. For example, if queries of sh.sales specify different dimensions in different orders, then you can cluster data in the sales table according to columns in these dimensions.

Attribute clustering is only available for direct path INSERT operations. It is ignored for conventional DML.

This section contains the following topics:

Advantages of Attribute-Clustered Tables

The primary benefit of attribute-clustered tables is I/O reduction, which can significantly reduce the I/O cost and CPU cost of table scans. I/O reduction occurs either with zones or by reducing physical I/O through closer physical proximity on disk for the clustered values.

An attribute-clustered table has the following advantages:

You can cluster fact tables based on dimension columns in star schemas.

In star schemas, most queries qualify dimension tables and not fact tables, so clustering by fact table columns is not effective. Oracle Database supports clustering on columns in dimension tables.

I/O reduction can occur in several different scenarios:

When used with Oracle Exadata Storage Indexes, Oracle In-Memory min/max pruning, or zone maps

In OLTP applications for queries that qualify a prefix and use attribute clustering with linear order

On a subset of the clustering columns for BY INTERLEAVED ORDER clustering

Attribute clustering can improve data compression, and in this way indirectly improve table scan costs.

When the same values are close to each other on disk, the database can more easily compress them.

Oracle Database does not incur the storage and maintenance cost of an index.

Oracle Database Data Warehousing Guide for more advantages of attribute-clustered tables

Join Attribute Clustered Tables

Attribute clustering that is based on joined columns is called join attribute clustering . In contrast with table clusters, join attribute clustered tables do not store data from a group of tables in the same database blocks.

For example, consider an attribute-clustered table, sales , joined with a dimension table, products . The sales table contains only rows from the sales table, but the ordering of the rows is based on the values of columns joined from products table. The appropriate join is executed during data movement, direct path insert, and CREATE TABLE AS SELECT operations. In contrast, if sales and products were in a standard table cluster, the data blocks would contain rows from both tables.

Oracle Database Data Warehousing Guide to learn more about join attribute clustering

I/O Reduction Using Zones

A zone is a set of contiguous data blocks that stores the minimum and maximum values of relevant columns. When a SQL statement contains predicates on columns stored in a zone, the database compares the predicate values to the minimum and maximum stored in the zone to determine which zones to read during SQL execution.

I/O reduction is the ability to skip table or index blocks that do not contain data that the database needs to satisfy the query. This reduction can significantly reduce the I/O and CPU cost of table scans.

Zone Maps

A zone map is an independent access structure that divides data blocks into zones. Oracle Database implements each zone map as a type of materialized view .

Whenever CLUSTERING is specified on a table, the database automatically creates a zone map on the specified clustering columns. The zone map correlates minimum and maximum values of columns with consecutive data blocks in the attribute-clustered table. Attribute-clustered tables use zone maps to perform I/O reduction.

You can create attribute-clustered tables that do not use zone maps. You can also create zone maps without attribute-clustered tables. For example, you can create a zone map on a table whose rows are naturally ordered on a set of columns, such as a stock trade table whose trades are ordered by time. You execute DDL statements to create, drop, and maintain zone maps.

Zone Maps: Analogy

For a loose analogy of zone maps, consider a sales manager who uses a bookcase of pigeonholes, which are analogous to data blocks.

Each pigeonhole has receipts (rows) describing shirts sold to a customer, ordered by ship date. In this analogy, a zone map is like a stack of index cards. Each card corresponds to a "zone" (contiguous range) of pigeonholes, such as pigeonholes 1-10. For each zone, the card lists the minimum and maximum ship dates for the receipts stored in the zone.

When someone wants to know which shirts shipped on a certain date, the manager flips the cards until she comes to the date range that contains the requested date, notes the pigeonhole zone, and then searches only pigeonholes in this zone for the requested receipts. In this way, the manager avoids searching every pigeonhole in the bookcase for the receipts.

Zone Maps: Example

This example illustrates how a zone map can prune data in a query whose predicate contains a constant.

Assume you create the following lineitem table:

The table lineitem contains 4 data blocks with 2 rows per block. Table 2-3 shows the 8 rows of the table.

Create a PostgreSQL Dataset

Before we can start practicing SQL queries, we’ll need a database to query against. In this case, we’ll create a PostgreSQL database from publicly available movie rental data using pgadmin for easier SQL administration and query execution.

Step 1 – Create the database by executing the following query:

Here we can see first hand the CREATE DATABASE command, which is used to create a database with a provided name.

Step 2 – Create the schema by downloading the schema definition and executing it with the query tool options:

Once loaded in pgadmin, we can use the UI to execute the script by clicking on the right-pointing forward arrow or by pressing F5.

Step 3 – Load the data into the database. Download the data using the curl tool, and insert it into the database using the psql tool:

Once loaded, we can use the pgAdmin UI to inspect the dataset tables. Here is an Entity Relationship Diagram (ERD) of the database, showing how each element in the database is related:

Indexing Columns

Typically developers index columns for three major reasons:

  1. To enforce unique values within a column
  2. To improve data access performance
  3. To prevent lock escalation when updating rows of tables that use declarative referential integrity

When a table is created and a PRIMARY KEY is specified an index is automatically created to enforce the primary key constraint. If you specific UNIQUE for a column when creating a column a unique index is also created. To see the indexes that already exist for a given table you can run the following dictionary query.

It is typically good form to index foreign keys, foreign keys are columns in a table that reference another table. In our EMPLOYEES and DEPARTMENTS table example the DEPTNO column in the EMPLOYEE table references the primary key of the DEPARTMENTS table.

We may also determine that the EMPLOYEE table will be frequently searched by the NAME column. To improve the performance searches and to ensure uniqueness we can create a unique index on the EMPLOYEE table NAME column.

Oracle provides many other indexing technologies including function based indexes which can index expressions, such as an upper function, text indexes which can index free form text, bitmapped indexes useful in data warehousing. You can also create indexed organized tables, you can use partition indexes and more. Sometimes it is best to have fewer indexes and take advantage of in memory capabilities. All of these topics are beyond the scope of this basic introduction.

6 Answers 6

Here are a few ways, each of which operates upon the individual component associations. In the following discussion, recall that when a key name is not a valid symbol we can write, for example, #["col_name"] instead of #col .

We can explicitly construct a new association that includes all of the old columns and adds a new one:

This has the disadvantage that we have to list all of the existing columns. To avoid this, we can use Append :

Should we wish to add multiple computed columns, we can use Join :

By exploiting the fact that <| . |> syntax can be nested:

. we can append columns to the dataset's associations using a shorter form:

2017 Update: It has been observed that the shorter form is not explictly mentioned in the documentation for Association (as of V11.1, see comments 1 and 2 for example). The documentation does mention that lists are "flattened out":

. and that all but the last occurrence of repeated keys are ignored:

The documentation also frequently says that associations can be used in place of lists in many functions. It should come as no surprise that Association itself allows us to use an association in place of a list:

This last expression is the "shorter form" from above.

Notwithstanding that the documentation strongly suggests that the short form is valid, I agree with commentators that it would be better if the documentation explicitly discussed the construction.

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