Backend flowchart

This material is referenced by a flowchart.

Initialization

bootstrap

Creates initial template database via initdb Because PostgreSQL requires access to system tables for almost every operation, getting those system tables in place is a problem. You can't just create the tables and insert data into them in the normal way, because table creation and insertion requires the tables to already exist. This code jams the data directly into tables using a special syntax used only by the bootstrap procedure.

main

Passes control to postmaster or postgres This checks the process name(argv[0]) and various flags, and passes control to the postmaster or postgres backend code.

postmaster

Controls postgres server startup/termination This creates shared memory, and then goes into a loop waiting for connection requests. When a connection request arrives, a postgres backend is started, and the connection is passed to it.

libpq

Backend libpq library routines This handles communication to the client processes.

Main Query Flow

tcop

Traffic cop, dispatches request to proper module This contains the postgres backend main handler, as well as the code that makes calls to the parser, optimizer, executor, and commands functions.

parser

Converts SQL query to query tree This converts SQL queries coming from libpq into command-specific structures to be used by the optimizer/executor, or commands routines. The SQL is lexically analyzed into keywords, identifiers, and constants, and passed to the parser. The parser creates command-specific structures to hold the elements of the query. The command-specific structures are then broken apart, checked, and passed to commands processing routines, or converted into Listsof Nodes to be handled by the optimizer and executor.

rewrite

Rule and view support

optimizer

Creates path and plan

This uses the parser output to generate an optimal plan for the executor.

optimizer_path

Creates path from parser output This takes the parser query output, and generates all possible methods of executing the request. It examines table join order, where clause restrictions, and optimizer table statistics to evaluate each possible execution method, and assigns a cost to each.

optimizer_geqo

Genetic query optimizer optimizer/path evaluates all possible ways to join the requested tables. When the number of tables becomes great, the number of tests made becomes great too. The Genetic Query Optimizer considers each table separately, then figures the most optimal order to perform the join. For a few tables, this method takes longer, but for a large number of tables, it is faster. There is an option to control when this feature is used.

optimizer_plan

Optimizes path output This takes the optimizer/path output, chooses the path with the least cost, and creates a plan for the executor.

optimizer_prep

Handle special plan cases This does special plan processing.

optimizer_util

Optimizer support routines This contains support routines used by other parts of the optimizer.

executor

Executes complex node plans from optimizer This handles select, insert, update, and delete statements. The operations required to handle these statement types include heap scans, index scans, sorting, joining tables, grouping, aggregates, and uniqueness.

Command Support

commands

Commands that do not require the executor These process SQL commands that do not require complex handling. It includes vacuum, copy, alter, create table, create type, and many others. The code is called with the structures generated by the parser. Most of the routines do some processing, then call lower-level functions in the catalog directory to do the actual work.

catalog

System catalog manipulation This contains functions that manipulate the system tables or catalogs. Table, index, procedure, operator, type, and aggregate creation and manipulation routines are here. These are low-level routines, and are usually called by upper routines that pre-format user requests into a predefined format.

access

Various data access methods These control the way data is accessed in heap, indexes, and transactions.

access_common

Common access routines

access_gin

Generalized inverted index access method

access_gist

generalized search tree access method

access_hash

hash access method

access_heap

heap is use to store data rows

access_index

used by all index types

access_nbtree

Lehman and Yao's btree management algorithm

access_spgist

Space-Partitioned GiST access method

access_transam

transaction manager (BEGIN/ABORT/COMMIT)

nodes

creation/manipulation of nodes and lists PostgreSQL stores information about SQL queries in structures called nodes. Nodes are generic containers that have a type field and then a type-specific data section. Nodes are usually placed in Lists. A List is container with an elem element, and a next field that points to the next List. These List structures are chained together in a forward linked list. In this way, a chain of List s can contain an unlimited number of Node elements, and each Node can contain any data type. These are used extensively in the parser, optimizer, and executor to store requests and data.

storage

Manages various storage systems These allow uniform resource access by the backend.

storage_buffer

Shared buffer pool manager

storage_file

File manager

storage_freespace

Free space map

storage_ipc

Semaphores and shared memory

storage_large_object

Large objects

storage_lmgr

Lock manager

storage_page

Page manager

storage_smgr

Storage/disk manager

utils

support routines

utils_adt

built-in data type routines This contains all the PostgreSQL builtin data types.

utils_cache

system/relation/function cache routines PostgreSQL supports arbitrary data types, so no data types are hard-coded into the core backend routines. When the backend needs to find out about a type, is does a lookup of a system table. Because these system tables are referred to often, a cache is maintained that speeds lookups. There is a system relation cache, a function/operator cache, and a relation information cache. This last cache maintains information about all recently-accessed tables, not just system ones.

error

error reporting routines Reports backend errors to the front end.

utils_fmgr

function manager This handles the calling of dynamically-loaded functions, and the calling of functions defined in the system tables.

utils_hash

hash routines for internal algorithms These hash routines are used by the cache and memory-manager routines to do quick lookups of dynamic data storage structures maintained by the backend.

utils_init

various initialization stuff

utils_mb

single and multibyte encoding

utils_misc

miscellaneous stuff

utils_mmgr

memory manager(process-local memory) When PostgreSQL allocates memory, it does so in an explicit context. Contexts can be statement-specific, transaction-specific, or persistent/global. By doing this, the backend can easily free memory once a statement or transaction completes.

Backend flowchart

Initialization

Main Query Flow

Command Support

Support Facilities

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