5.1. Table Basics

A table in a relational database is much like a table on paper: It consists of rows and columns. The number and order of the columns is fixed, and each column has a name. The number of rows is variable — it reflects how much data is stored at a given moment. SQL does not make any guarantees about the order of the rows in a table. When a table is read, the rows will appear in an unspecified order, unless sorting is explicitly requested. This is covered in Chapter 7. Furthermore, SQL does not assign unique identifiers to rows, so it is possible to have several completely identical rows in a table. This is a consequence of the mathematical model that underlies SQL but is usually not desirable. Later in this chapter we will see how to deal with this issue.

Each column has a data type. The data type constrains the set of possible values that can be assigned to a column and assigns semantics to the data stored in the column so that it can be used for computations. For instance, a column declared to be of a numerical type will not accept arbitrary text strings, and the data stored in such a column can be used for mathematical computations. By contrast, a column declared to be of a character string type will accept almost any kind of data but it does not lend itself to mathematical calculations, although other operations such as string concatenation are available.

PostgreSQL includes a sizable set of built-in data types that fit many applications. Users can also define their own data types. Most built-in data types have obvious names and semantics, so we defer a detailed explanation to Chapter 8. Some of the frequently used data types are integer for whole numbers, numeric for possibly fractional numbers, text for character strings, date for dates, time for time-of-day values, and timestamp for values containing both date and time.

To create a table, you use the aptly named CREATE TABLE command. In this command you specify at least a name for the new table, the names of the columns and the data type of each column. For example:

CREATE TABLE my_first_table (
    first_column text,
    second_column integer
);

This creates a table named my_first_table with two columns. The first column is named first_column and has a data type of text; the second column has the name second_column and the type integer. The table and column names follow the identifier syntax explained in Section 4.1.1. The type names are usually also identifiers, but there are some exceptions. Note that the column list is comma-separated and surrounded by parentheses.

Of course, the previous example was heavily contrived. Normally, you would give names to your tables and columns that convey what kind of data they store. So let's look at a more realistic example:

CREATE TABLE products (
    product_no integer,
    name text,
    price numeric
);

(The numeric type can store fractional components, as would be typical of monetary amounts.)

There is a limit on how many columns a table can contain. Depending on the column types, it is between 250 and 1600. However, defining a table with anywhere near this many columns is highly unusual and often a questionable design.

In Postgres-XC, each table can be distributed or replicated among Datanodes. By distributing tables, each query, if the target is determined from the incoming statement, can be handled by single or small number of Datanodes and more transactions can be handled in parallel. If you replicate tables, and if they're more read than written, transactions reading such tables can be handled in parallel.

When you distribute a table, you can choose almost any column of a fundamental data type as distribution column. For details, please refer to CREATE TABLE. Datanode for specific row is determined based upon the value of the distribution column. By default, distribution column is the first column you specified in CREATE TABLE statement and the column value is used to generate hash value as an index for Datanode which accommodate the row. You can choose another distribution method such as MODULO and ROUNDROBIN. To specify what column to choose as the distribution column and what value test to choose, you can do as follows:

CREATE TABLE products (
    product_no integer,
    name text,
    price numeric
) DISTRIBUTE BY HASH(product_no);

In this case, the column product_no is chosen as the distribute column and the target Datanode of each row is determined based upon the hash value of the column. You can use MODULO to specify modulo to test and determine the target Datanode. You can also specify ROUNDROBIN to determine the Datanode by the order each row is inserted.

Please note that with HASH and MODULO, Coordinator have a chance to determine the location of target row from incoming statement. This reduces the number of involved Datanodes and can increase the number of transaction handled in parallel.

On the other hand, if exact value cannot be obtained from incoming statement, for example, in the case of floating point number, Postgres-XC may fail to find precise target Datanode and it is not recommended to use such column as a distribution column.

To replicate a table into all the Datanodes, specify DISTRIBUTE BY REPLICATION as follows:

CREATE TABLE products (
    product_no integer,
    name text,
    price numeric
) DISTRIBUTE BY REPLICATION;

If you no longer need a table, you can remove it using the DROP TABLE command. For example:

DROP TABLE my_first_table;
DROP TABLE products;

Attempting to drop a table that does not exist is an error. Nevertheless, it is common in SQL script files to unconditionally try to drop each table before creating it, ignoring any error messages, so that the script works whether or not the table exists. (If you like, you can use the DROP TABLE IF EXISTS variant to avoid the error messages, but this is not standard SQL.)

If you need to modify a table that already exists, see Section 5.5 later in this chapter.

With the tools discussed so far you can create fully functional tables. The remainder of this chapter is concerned with adding features to the table definition to ensure data integrity, security, or convenience. If you are eager to fill your tables with data now you can skip ahead to Chapter 6 and read the rest of this chapter later.