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A Suffix Tree is a compressed tree containing all the suffixes of the given (usually long) text string T of length n characters (n can be in order of hundred thousands characters).

The positions of each suffix in the text string T are recorded as integer indices at the leaves of the Suffix Tree whereas the path labels (concatenation of edge labels starting from the root) of the leaves describe the suffixes.

Suffix Tree provides a particularly fast implementation for many important (long) string operations.

This data structure is very related to the Suffix Array data structure. Both data structures are usually studied together.

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The suffix i (or the i-th suffix) of a (usually long) text string T is a 'special case' of substring that goes from the i-th character of the string up to its last character.

For example, if T = "STEVEN$", then suffix 0 of T is "STEVEN$" (0-based indexing), suffix 2 of T is "EVEN$", suffix 4 of T is "EN$", etc.

Pro-tip 1: Since you are not logged-in, you may be a first time visitor (or not an NUS student) who are not aware of the following keyboard shortcuts to navigate this e-Lecture mode: [PageDown]/[PageUp] to go to the next/previous slide, respectively, (and if the drop-down box is highlighted, you can also use [→ or ↓/← or ↑] to do the same),and [Esc] to toggle between this e-Lecture mode and exploration mode.


The visualization of Suffix Tree of a string T is basically a rooted tree where path label (concatenation of edge label(s)) from root to each leaf describes a suffix of T. Each leaf vertex is a suffix and the integer value written inside the leaf vertex (we ensure this property with terminating symbol $) is the suffix number.

An internal vertex will branch to more than one child vertex, therefore there are more than one suffix from the root to the leaves via this internal vertex. The path label of an internal vertex is a common prefix among those suffix(es).

Pro-tip 2: We designed this visualization and this e-Lecture mode to look good on 1366x768 resolution or larger (typical modern laptop resolution in 2021). We recommend using Google Chrome to access VisuAlgo. Go to full screen mode (F11) to enjoy this setup. However, you can use zoom-in (Ctrl +) or zoom-out (Ctrl -) to calibrate this.


The Suffix Tree above is built from string T = "GATAGACA$" that have these 9 suffixes:


Now verify that the path labels of suffix 7/6/2 are "A$"/"CA$"/"TAGACA$", respectively (there are 6 other suffixes). The internal vertices with path label "A"/"GA" branch out to 4 suffixes {7, 5, 3, 1}/2 suffixes {4, 0}, respectively (we ignore the trivial internal vertex = root vertex that branches out to all 9 suffixes).

Pro-tip 3: Other than using the typical media UI at the bottom of the page, you can also control the animation playback using keyboard shortcuts (in Exploration Mode): Spacebar to play/pause/replay the animation, / to step the animation backwards/forwards, respectively, and -/+ to decrease/increase the animation speed, respectively.


In order to ensure that every suffix of the input string T ends in a leaf vertex, we enforce that string T ends with a special terminating symbol '$' that is not used in the original string T and has ASCII value lower than the lowest allowable character in T (which is character 'A' in this visualization). This way, edge label '$' always appear at the leftmost edge of the root vertex of this Suffix Tree visualization.

For the Suffix Tree example above (for T = "GATAGACA$"), if we do not have terminating symbol '$', notice that suffix 7 "A" (without the '$') does NOT end in a leaf vertex and can complicate some operations later.


As we have ensured that all suffixes end at a leaf vertex, there are at most n leaves/suffixes in a Suffix Tree. All internal vertices (including the root vertex if it is an internal vertex) are always branching thus there can be at most n-1 such vertices, as shown with one of the extreme test case on the right.

The maximum number of vertices in a Suffix Tree is thus = n (leaves) + (n-1) internal vertices = 2n-1 = O(n) vertices. As Suffix Tree is a tree, the maximum number of edges in a Suffix Tree is also (2n-1)-1 = O(n) edges.


When all the characters in string T is all distinct (e.g., T = "ABCDE$"), we can have the following very short Suffix Tree with exactly n+1 vertices (+1 due to root vertex).


All available operations on the Suffix Tree in this visualization are listed below:

  1. Build Suffix Tree (instant/details omitted) — instant-build the Suffix Tree from string T.
  2. Search — Find the vertex in Suffix Tree of a (usually longer) string T that has path label containing the (usually shorter) pattern/search string P.
  3. Longest Repeated Substring (LRS) — Find the deepest (the one that has the longest path label) internal vertex (as that vertex shares common prefix between two (or more) suffixes of T).
  4. Longest Common Substring (LCS) — Find the deepest internal vertex that contains suffixes from two different original strings.

There are a few other possible operations of Suffix Tree that are not included in this visualization.


In this visualization, we only show the fully constructed Suffix Tree without describing the details of the O(n) Suffix Tree construction algorithm — it is a bit too complicated. Interested readers can explore this instead.

We limit the input to only accept 12 (cannot be too long due to the available drawing space — but in the real application of Suffix Tree, n can be in order of hundred thousand to million characters) UPPERCASE (we delete your lowercase input) alphabet and the special terminating symbol '$' characters (i.e., [A-Z$]). If you do not write a terminating symbol '$' at the back of your input string, we will automatically do so. If you place a '$' in the middle of the input string, they will be ignored. And if you enter an empty input string, we will resort to the default "GATAGACA$".

For convenience, we provide a few classic test case input strings usually found in Suffix Tree/Array lectures, but to showcase the strength of this visualization tool, you are encouraged to enter any 12-characters string of your choice (ending with character '$').


Assuming that the Suffix Tree of a (usually longer) string T (of length n) has been built, we want to find all occurrences of pattern/search string P (of length m).

To do this, we search for the vertex x in the suffix Tree of T which has path label (concatenation of edge label(s) from the root to x) where P is the prefix of that path label. Once we find this vertex x, all the leaves in the subtree rooted at x are the occurrences.

Time complexity: O(m+k) where k is the total number of occurrences.

For example, on the Suffix Tree of T = "GATAGACA$" above, try these scenarios:

  1. P is a full match with the path label of vertex x:
    Search("A"), occurrences = {7, 5, 3, 1} or Search("GA"), occurrences = {4, 0}
  2. P is a partial match with the path label of vertex x:
    Search("T"), occurrences = {2} or Search("GAT"), occurrences = {0}
  3. P is not found in T:
    Search("WALDO"), occurrences = {NIL}

Assuming that the Suffix Tree of a (usually longer) string T (of length n) has been built, we can find the Longest Repeated Substring (LRS) in T by simply finding the deepest (the one that has the longest path label) internal vertex of the Suffix Tree of T.

This is because each internal vertex of the Suffix Tree of T branches out to at least two (or more) suffixes, i.e., the path label (common prefix of these suffixes) are repeated.

The deepest (the one that has the longest path label) internal vertex is the required answer, which can be found in O(n) with a simple tree traversal.

Without further ado, try LRS("GATAGACA$"). We have LRS = "GA".

It is possible that T contains more than one LRS, e.g., try LRS("BANANABAN$").
We have LRS = "ANA" (actually overlap) or "BAN" (without overlap).


This time, we need two input strings T1 and T2 that terminate with symbol '$'/'#', respectively. We then create the generalized Suffix Tree of these two strings T1+T2 in O(n) where n = n1+n2 (sum of the length of the two strings). We can find the Longest Common Substring (LCS) of those two strings T1 and T2 by simply finding the deepest and valid internal vertex of the generalized Suffix Tree of T1+T2.

To be a valid internal vertex for consideration as an LCS candidate, an internal vertex must represents suffixes from both strings, i.e., a common substring found in both T1 and T2.

Then, since an internal vertex of the Suffix Tree of T branches out to at least two (or more) suffixes, i.e., the path label (common prefix of these suffixes) are repeated. If that internal vertex is also a valid internal vertex, then it is a common substring that is repeated.

The valid and deepest (the one that has the longest path label) internal vertex is the required answer, which can be found in O(n) with a simple tree traversal.

Without further ado, try LCS(T1,T2) on the generalized Suffix Tree of string T1 = "GATAGACA$" and T2 = "CATA#" (notice that the UI will change to generalized Suffix Tree version). We have LCS = "ATA".


There are a few other things that we can do with Suffix Tree like "Finding Longest Repeated Substring without overlap", "Finding Longest Common Substring of ≥ 2 strings", etc, but we will keep that for later.

We will continue the discussion of this String-specific data structure with the more versatile to Suffix Array data structure.

You have reached the last slide. Return to 'Exploration Mode' to start exploring!

Note that if you notice any bug in this visualization or if you want to request for a new visualization feature, do not hesitate to drop an email to the project leader: Dr Steven Halim via his email address: stevenhalim at gmail dot com.














T =


T1 =
T2 =


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VisuAlgo于2011年由Steven Halim博士创建,是一个允许学生以自己的速度自学基础知识,从而更好地学习数据结构与算法的工具。
VisuAlgo包含许多高级算法,这些算法在Steven Halim博士的书(“Competitive Programming”,与他的兄弟Felix Halim博士合作)和其他书中有讨论。今天,一些高级算法的可视化/动画只能在VisuAlgo中找到。
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Dr Steven Halim, Senior Lecturer, School of Computing (SoC), National University of Singapore (NUS)
Dr Felix Halim, Senior Software Engineer, Google (Mountain View)

本科生研究人员 1 (Jul 2011-Apr 2012)
Koh Zi Chun, Victor Loh Bo Huai

最后一年项目/ UROP学生 1 (Jul 2012-Dec 2013)
Phan Thi Quynh Trang, Peter Phandi, Albert Millardo Tjindradinata, Nguyen Hoang Duy

最后一年项目/ UROP学生 2 (Jun 2013-Apr 2014)
Rose Marie Tan Zhao Yun, Ivan Reinaldo

本科生研究人员 2 (May 2014-Jul 2014)
Jonathan Irvin Gunawan, Nathan Azaria, Ian Leow Tze Wei, Nguyen Viet Dung, Nguyen Khac Tung, Steven Kester Yuwono, Cao Shengze, Mohan Jishnu

最后一年项目/ UROP学生 3 (Jun 2014-Apr 2015)
Erin Teo Yi Ling, Wang Zi

最后一年项目/ UROP学生 4 (Jun 2016-Dec 2017)
Truong Ngoc Khanh, John Kevin Tjahjadi, Gabriella Michelle, Muhammad Rais Fathin Mudzakir

最后一年项目/ UROP学生 5 (Aug 2021-Dec 2022)
Liu Guangyuan, Manas Vegi, Sha Long, Vuong Hoang Long

List of translators who have contributed ≥100 translations can be found at statistics page.



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List of Publications

This work has been presented briefly at the CLI Workshop at the ICPC World Finals 2012 (Poland, Warsaw) and at the IOI Conference at IOI 2012 (Sirmione-Montichiari, Italy). You can click this link to read our 2012 paper about this system (it was not yet called VisuAlgo back in 2012) and this link for the short update in 2015 (to link VisuAlgo name with the previous project).

This work is done mostly by my past students. 

Bug Reports or Request for New Features

VisuAlgo is not a finished project. Dr Steven Halim is still actively improving VisuAlgo. If you are using VisuAlgo and spot a bug in any of our visualization page/online quiz tool or if you want to request for new features, please contact Dr Steven Halim. His contact is the concatenation of his name and add gmail dot com.


Version 1.1 (Updated Fri, 14 Jan 2022).

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