What is Genji?

• 1: Getting started
• 2: Essentials
• 2.1: SQL Introduction
• 2.2: Documents
• 2.3: Data Types
• 2.4: Expressions
• 3: Reference
• 3.1: ALTER TABLE
• 3.2: CREATE INDEX
• 3.3: CREATE SEQUENCE
• 3.4: Transactions
• 3.5: CREATE TABLE
• 3.6: DELETE
• 3.7: DROP INDEX
• 3.8: DROP SEQUENCE
• 3.9: DROP TABLE
• 3.10: EXPLAIN
• 3.11: INSERT
• 3.12: REINDEX
• 3.13: SELECT
• 3.14: UPDATE

1 - Getting started

Prerequisites

Genji requires at least Go 1.18.

Installation

To install the Genji database, run this command:

go get github.com/genjidb/genji

Golang API documentation

To learn how to embed Genji in your Go code, follow the instructions in the Go package documentation.

Try it out!

To try Genji without writing code, you can use the Genji command-line shell.

First, install it:

go install github.com/genjidb/genji/cmd/genji@latest

To open an in-memory database, simply type:

genji

You can then enter your SQL queries directly in the shell.

It is also possible to create an on-disk database by specifying a directory:

genji mydb

Next step

Once Genji is setup, follow the Genji SQL chapter to learn how to run queries.

2 - Essentials

2.1 - SQL Introduction

Tables

Though Genji stores its data in tables, there is no concept of rows or columns. In Genji:

• a table is a collection of documents
• a document is a collection of fields
• a field is a key-value pair

Each document is assigned a primary key, which is a unique identifier.

Schemas

Like relational databases, tables can have a schema to control and validate data. There are three kinds of schema that can be defined in Genji:

• strict schemas
• partial schemas
• no schema (or schemaless)

Strict schemas

This kind of schema is similar to the ones found in other databases: It defines all the fields of every document.

But unlike other databases, it has some really interesting properties that are taylored for documents:

• It is possible to define constraints on nested fields
• When inserting a document that contains fields that where not defined in the schema, these fields are ignored

Let’s see an example:

CREATE TABLE users (
    id        INTEGER PRIMARY KEY,
    name      TEXT NOT NULL,
    age       INTEGER,
    email     TEXT NOT NULL UNIQUE
    address (
      zipCode TEXT
    ),

    CHECK(age >= 0)
)

This will create a table users with the following constraints:

• All documents must have a non-empty id field, whose type can be converted to an integer. This field will be used as the primary key of the table and will be stored as an integer.
• All documents must have a non-empty name field that can be converted to TEXT.
• If a document has an age field, it will be converted to an integer. The field has to be positive.
• All documents must have a non-empty email field that can be converted to TEXT and it must be unique across the whole table.
• If a document has an address field and its value is a document with a zipCode field, then its value will be converted to TEXT.

Inserting documents on strict tables can be done in multiple ways:

INSERT INTO users
  (id, name, age, email, address)
  VALUES (
    1,
    'Jim',
    10,
    'jim@host.com',
    {
      zipCode: '12345'
    }
  )

-- or

INSERT INTO users VALUES {
  id: 1,
  name: 'Jim',
  age: 10,
  email: 'jim@host.com',
  address: {
    zipCode: '12345'
  }
}

Partial schemas

When dealing with data coming from different sources (APIs, files, streams, etc.), is might be useful to define constraints only on a set of fields, while storing the other fields as-is. These kind of schemas are called partial schemas.

Let’s create a table that can be used to store some Genji’s Github issues consumed from the Github API:

CREATE TABLE github_issues (
  id            INTEGER PRIMARY KEY,
  title         TEXT,
  user (
    id          INTEGER UNIQUE
    site_admin  BOOLEAN,

    ...
  ),

  ...
)

By using the ... notation, it is possible to tell Genji to insert all fields of incoming documents but to apply constraints only to certain fields. It also works on nested documents.

Here is how to use the Genji CLI to populate this table:

curl https://api.github.com/repos/genjidb/genji/issues | genji insert --db mydb -t github_issues

Schemaless tables

Unlike relational databases, it is also possible to define schemaless tables. Documents stored in schemaless tables can be completely different from one another.

CREATE TABLE teams;

-- equivalent to

CREATE TABLE teams (...);

Inserting documents can then be done with no constraints:

INSERT INTO teams (name, league) VALUES ('Real Madrid', 'Ligua');
INSERT INTO teams (name, league, members) VALUES ('PSG', 'Ligue 1', ['Messi']);

Dropping a table

CREATE TABLE will return an error if the table already exists.

To remove a table and all of its content, use the DROP TABLE command:

DROP TABLE users;

This will remove the users table and all of its documents. If DROP TABLE is called on a non-existing table, it will return an error.

Inserting documents

The INSERT statement is used to add documents to tables. It was designed with two goals in mind:

• provide an API familiar to users used to other SQL databases
• provide an alternative API designed to simplify inserting complex documents

Here is the first form:

INSERT INTO countries (name, population) VALUES ('France', 67900000);

-- if the countries table has a strict or partial schema, field names can be omitted.
INSERT INTO countries VALUES ('France', 67900000);

Because Genji is a document database, this form also supports nested fields and arrays:

INSERT INTO bands (name, members, albums)
  VALUES (
    "Guns N' Roses",
    ["Axl Rose", "Slash", "Steven Adler", "Duff McKagan"],
    [
      {
      name: "Appetite for Destruction",
      releaseYear: "1987"
      },
      {
        name: "G N' R Lies",
        releaseYear: "1988"
      }
    ]
  );

The second form uses the document literal notation. It is useful when inserting complex documents that would make the first form too verbose:

INSERT INTO bands VALUES {
  "name": "Guns N' Roses",
  "members": ["Axl Rose", "Slash", "Steven Adler", "Duff McKagan"],
  "albums": [
      {
        name: "Appetite for Destruction",
        releaseYear: "1987"
      },
      {
        name: "G N' R Lies",
        releaseYear: "1988"
      }
  ]
}

Selecting documents

Querying documents from a table can be achieved by using the SELECT statement. The output of SELECT statements is a strean of documents that can be decoded and represented in any form.

Here is an example of selecting all documents of the users table:

SELECT * FROM players;

Here is a json representation of the output

{
    "name": "Rafael Nadal",
    "age": 36,
    "nationality": "Spain",
    "career": {
      "australia": 2,
      "france": 14,
      "wimbledon": 2,
      "us": 4
    },
    "coach": [
      "Francisco Roig",
      "Carlos Moyá",
      "Marc López"
    ]
}
{
    "name": "Roger Federer",
    "age": 40,
    "nationality": "Switzerland",
    "career": {
      "australia": 6,
      "france": 1,
      "wimbledon": 8,
      "us": 5
    },
    "coach": [
      "Ivan Ljubičić",
      "Severin Lüthi",
    ]
}
{
    "name": "Andrew Barron Murray",
    "coach": [
      "Ivan Lendl"
    ]
}

Let’s break it down:

• SELECT: Run the SELECT command
• *: This is the projection, it indicates how to build the documents returned by the result of the query. Here, we are using a special projection called the wildcard, which is a way to tell Genji to simply return all of the fields of each document.
• FROM players: Indicates from which table we want to query the data.

Understanding projections

Now, let’s query only the name and age of each player:

SELECT name, age FROM players;

{
  "name": "Rafael Nadal",
  "age": 36
}
{
  "name": "Roger Federer",
  "age": 40
}
{
  "name": "Andrew Barron Murray",
  "age": null
}

The result contains three documents, all of them have a name and age fields.

A projection guarantees that all the documents returned by the query will contain the selected fields, even if the original documents don’t have that information. In our example, the Andrew Barron Murray document doesn’t have an age field, so its projected value is null. The only exception is for the * wildcard, which projects all the fields of the original document.

Querying nested fields

Let’s determine how many French Open each of them has won in their career:

SELECT name, career.france FROM players;

{
  "name": "Rafael Nadal",
  "career.france": 14
}
{
  "name": "Roger Federer",
  "career.france": 1
}
{
  "name": "Andrew Barron Murray",
  "career.france": null
}

In this example, we used a path to select the career.france field.

Let’s add the information about the first coach of each player:

SELECT name, career.france, coach[0] FROM players;

{
  "name": "Rafael Nadal",
  "career.france": 14,
  "coach[0]": "Francisco Roig"
}
{
  "name": "Roger Federer",
  "career.france": 1,
  "coach[0]": "Ivan Ljubičić"
}
{
  "name": "Andrew Barron Murray",
  "career.france": null,
  "coach[0]": "Ivan Lendl"
}

coach[0] is a notation that indicates to select the element at index 0 of the coach array.

Filter documents

Until now, we always performed our queries on every document of the table. Let’s only query those whose career document is set.

SELECT name FROM players WHERE career IS NOT NULL;

{
  "name": "Rafael Nadal"
}
{
  "name": "Roger Federer"
}

This time, the result contains only two documents.

The WHERE clause allows filtering the documents returned. To do that, it evaluates an expression on every document:

• if the result of the evaluation is truthy, the document is selected
• if the result of the evaluation is falsy, the document is filtered out

SELECT name, age FROM players WHERE age < 40;

{
  "name": "Rafael Nadal",
  "age": 36
}

In this example, only Rafael Nadal satisfies the query:

• Roger Federer’s age is 40 which is not < 40
• Andrew Barron Murray’s age is null, which is also not < 40

Filtering on values in nested objects

We can filter on values in nested arrays using the IN operator:

SELECT name, coach FROM players WHERE 'Ivan Ljubičić' IN coach;

{
  "name": "Roger Federer",
  "coach": ["Ivan Ljubičić", "Severin Lüthi"]
}

And values in nested documents:

SELECT name, career.wimbledon AS wimbledon FROM players WHERE career.wimbledon > 3;

{
  "name": "Roger Federer",
  "wimbledon": 8
}

Ordering results

The order in which documents are returned when reading a table is not guaranteed unless sorted explicitly.

To control the order of the results, we need to use the ORDER BY clause

SELECT name, career.australia AS australia FROM players ORDER BY career.australia;

{
  "name": "Andrew Barron Murray",
  "australia": null
}
{
  "name": "Rafael Nadal",
  "australia": 2
}
{
  "name": "Roger Federer",
  "australia": 6
}

The order in which documents appear depends on three factors:

• the direction of the order
• the type of the field used for ordering
• the value of the field used for ordering

By default, the direction is ascending, from the smallest value to the highest.

When it comes to ordering, there is a hierarchy between types:

NULL < BOOLEAN < numbers < TEXT or BLOB

In the example above, the career field of Andrew Barron Murray doesn’t exist, so it is treated as null, and then appears first in the result.

Then, Rafael Nadal and Roger Federer have an career.australia field which is an INTEGER, there are compared with each other and returned in ascending order.

The direction can be controlled by using ASC or DESC clauses.

SELECT name, career.australia AS australia FROM players ORDER BY career.australia ASC;

// returns the same results as above

SELECT name, career.australia AS australia FROM players ORDER BY career.australia DESC;

{
  "name": "Roger Federer",
  "australia": 6
}
{
  "name": "Rafael Nadal",
  "australia": 2
}
{
  "name": "Andrew Barron Murray",
  "australia": null
}

Using functions

Projections can also use functions to add more power to the queries.

Select the primary key

Every document has a primary key, which is a unique value. When a document is inserted without an explicit primary key, an implicit docid is created automatically. Implicit primary keys don’t appear in the results though, even when using SELECT *. To select them, we can use the pk() function.

SELECT pk(), name FROM players;

{
  "pk()": [
    1
  ],
  "name": "Rafael Nadal"
}
{
  "pk()": [
    2
  ],
  "name": "Roger Federer"
}
{
  "pk()": [
    3
  ],
  "name": "Andrew Barron Murray"
}

Because the primary key can be composite, pk() always returns an array.

Here is an example with another table with a composite primary key

CREATE TABLE cars (
  brand TEXT,
  name TEXT,
  year INTEGER,

  PRIMARY KEY (brand, name)
);

INSERT INTO cars VALUES ('Ford', 'Mustang', 1965);
INSERT INTO cars VALUES ('Peugeot', '205', 1984);

SELECT pk(), name FROM cars;

{
  "pk()": [
    "Ford",
    "Mustang"
  ],
  "name": "Mustang"
}
{
  "pk()": [
    "Peugeot",
    "205"
  ],
  "name": "205"
}

Other SELECT clauses

The SELECT statement is very rich and can do much more. Read the SELECT reference for more information.

Updating documents

The UPDATE statement makes it possible to update one or more documents in a table.

Consider a table users with the following documents in it.

{
    "name": "Thor",
    "age": 1000
}
{
    "name": "Hulk",
    "group": "Avengers",
    "age": 42
}

UPDATE users SET group = "Avengers"

Let’s break it down:

• UPDATE users runs the UPDATE statement on the users table
• SET indicates the list of changes we want to perform
• group = "Avengers" sets the group field of the document to the value “Avengers”

Without a WHERE clause, this statement will run on all the documents of the table. Here is the state of the table after running this command:

{
    "name": "Thor",
    "age": 1000,
    "group": "Avengers"
}
{
    "name": "Hulk",
    "age": 42,
    "group": "Avengers"
}

The first document didn’t have a group field before. It’s because the SET clause actually sets fields in the document, regardless of their existence. This is a good way to add new fields to documents.

Since we can add or modify fields using the SET clause, it is also possible to delete fields using the UNSET clause:

UPDATE users UNSET age;

This will delete the age field from all the documents. If the field doesn’t exist it does nothing.

To update only a subset of documents, we can use the WHERE clause. In the following example, only the documents that satisfy the age = 2 condition will be updated.

UPDATE users UNSET group WHERE age = 2;

Deleting documents

Documents can be deleted using the DELETE statement.

Let’s start with the simplest form:

DELETE FROM products;

This command deletes all the documents of the products table.

To delete only a few documents, use the WHERE clause:

DELETE FROM products WHERE sales_count < 1000;

For every document, the WHERE clause evaluates any expression that follows, here sales_count < 1000. If the result is truthy, the document gets deleted.

The DELETE statement doesn’t return an error if no document matches the WHERE clause, or if there aren’t any document in the table.

Using indexes

Indexes are created using the CREATE INDEX statement.

-- create a named index
CREATE INDEX user_country_idx ON users(country);

-- enforce uniqueness
CREATE UNIQUE INDEX user_email_idx ON users(email);

-- index name can be ommitted, let the engine generate one for you
CREATE UNIQUE INDEX ON users(username);

-- it is possible to create indexes on nested fields
CREATE INDEX ON players(career.us);

-- composite indexes are also supported
CREATE INDEX ON players(career.us, name);

To delete indexes, use the DROP INDEX statement

DROP INDEX user_email_idx;

2.2 - Documents

Genji stores records as documents. A document is an object that contains pairs that associate a string field to a value of any type. Genji SQL represents documents as JSON objects, though they support far more types.

Here is a JSON representation of the structure of a document:

{
    field1: value1,
    field2: value2,
    field3: value3,
    ...
}

Example of a document using Genji SQL syntax:

{
    name: "Nintendo Switch",
    price: {
        base: 379.99,
        vat: 20,
        total: base + base * vat / 100
    },
    brand: "Nintendo",
    "top-selling-games": [
        "Mario Odyssey",
        "Zelda Breath of the Wild"
    ]
}

Each field name must be a string, but values can be of any type, including another document, an array or an expression.

Any JSON object is a valid document and can be inserted as-is.

Field names

Field names can be any string, with only one exception: they cannot be empty.

Paths

A path is a way to refer to fields of a document or elements of an array.

Given the following document:

{
    "name": "Foo",
    "address": {
        "city": "Lyon",
        "zipcode": "69001"
    },
    "friends": [
      {
        "name": "Bar",
        "address": {
            "city": "Paris",
            "zipcode": "75001"
        }
      },
        {
          "name": "Baz",
          "address": {
              "city": "Ajaccio",
              "zipcode": "20000"
          },
          "favorite game": "FF IX"
        }
    ]
}

Accessing a top-level field can be achieved by simply referring to its name.

Example: name will evaluate to "Foo".

To access a nested field, either concatenate the fields with the . character, or use the [] notation

Examples: address.city will evaluate to "Lyon" Examples: address["city"] will evaluate to "Lyon"

To access an element of an array, use the index of the element

Examples:

• friends[0] will evaluate to {"name": "Bar","address": {"city":"Paris","zipcode": "75001"}}
• friends[1].name will evaluate to "Baz"
• friends[1]."favorite game" will evaluate to "FF IX"

2.3 - Data Types

Genji provides a list of simple data types to store and manipulate data.

The case of NULL

In Genji, NULL is treated as both a value and a type. It represents the absence of data, and is returned in various cases:

• when selecting a field that doesn’t exists
• when selecting a field whose value is NULL
• as the result of the evaluation of an expression

Conversion

Whenever Genji needs to manipulate data of different types, depending on the situation it will rely on either:

• explicit conversion: The source type and destination type are clearly identified. Ex: When inserting data to field with a constraint or when doing a CAST.
• implicit conversion: Two values of different types need to be compared or used by an operator during the evaluation of an expression

Explicit conversion

Explicit conversion is used when we want to convert a value of a source type into a target type. However, Genji types are not all compatible with one another, and when a user tries to convert them, Genji returns an error. Here is a table describing type compatibility.

Implicit conversion

There is only one kind of implicit conversion: INTEGER to DOUBLE. This usually takes place during the evaluation of an expression involving INTEGER and DOUBLE values. No other conversion is applied unless it’s explicit.

2.4 - Expressions

Expressions are components that can be evaluated to a value.

Example:

1 + 1 # expression
-> 2  # result

An expression can be found in two forms:

• unary: meaning it contains only one component
• binary: meaning it contains two expressions, or operands, and one operator. i.e. <expr> <operator> <expr>

Example:

/* Unary expressions */
1
name
"foo"

/* Binary expressions */
age >= 18
1 AND 0

Here is a list of all supported expressions:

Literal value

Any literal evaluates to the closest compatible type.

Integer literal

An integer literal is a sequence of characters that only contain digits. They may start with a + or - sign.

123456789
+100
-455

Integer literals are evaluated into the INTEGER type.

If an integer is bigger than the maximum value of a 64 bit integer or smaller than the minimum 64 bit integer value, it will be evaluated as a DOUBLE.

Number literal

A number literal is a sequence of characters that contains three parts:

• a sequence of digits
• a decimal point (i.e. .)
• a sequence of digits

They may start with a + or a - sign.

123.456
+3.14
-1.0

Number literals are evaluated to the DOUBLE type.

String literal

A string literal is a sequence of utf-8 encoded characters surrounded by double or single quotes. They may contain any unicode character or escaped single or double quotes (i.e \' or \").

"l'école des fans"
'(╯ಠ_ಠ）╯︵ ┳━┳'
'foo \''

String literals are evaluated to the TEXT type.

Blob literal

A blob literal starts with \x followed by a list of hexadecimal characters which is then decoded into raw bytes by Genji.

Blob literals are evaluated to the BLOB type.

Bool literal

A boolean literal is any sequence of character that is written as true or false, regardless of the case.

true
false
TRUE
FALSE
tRUe
FALse

Boolean literals are evaluated into the BOOL type.

Array literal

An array literal is any sequence of character that starts and ends with either:

• [ and ]
• ( and )

and that contains a coma-separated list of expressions.

[1.5, "hello", 1 > 10, [true, -10], {foo: "bar"}]

Array literals are evaluated into the ARRAY type.

Document literal

A document is any sequence of character that starts and ends with { and } and that contains a list of pairs. Each pair associates an identifier with an expression, both separated by a colon. Each pair must be separated by a coma.

{
    foo: 1,
    bar: "hello",
    baz: true AND false,
    "long field": {
        a: 10
    }
}

In a document, the identifiers are referred to as fields. In the example above, the document has four top-level fields (foo, bar, baz and long field) and one nested field a.

Note that any JSON object is a valid document.

Document literals are evaluated into the DOCUMENT type.

Identifier

Identifiers are a sequence of characters that refer to table names, field names and index names.

Identifiers may be unquoted or surrounded by backquotes. Depending on that, different rules may apply.

foo
_foo_123_
`頂きます (*｀▽´)_旦~~`
`foo \` bar`

Field path

A field path is any sequence of characters that contains one or more identifiers separated by dots or square brackets.

foo
foo.bar[10]
foo["long field"][0].bat.`other long field`

Depending on the context, a single identifier with no dot or square bracket will be parsed as an identifier or as a field path.

Field paths are evaluated into the value they refer to. They are used to select a value from a document. Their type will depend on the type of the value extracted from the document.

Given the following document:

{
    "recipes": 10,
    "cooking-time": {
        "eggs": [3, 6, 9]
    },
}

Here are examples on how field paths are evaluated:

recipes
-> 10

`cooking-time`
->  {
        "eggs": [
            3,
            6,
            9
        ]
    }

`cooking-time`.eggs[2]
-> 9

`cooking-time`.eggs[10]
-> NULL

Parameter

A parameters is an expressions used to represent a value passed when the query is evaluated. Genji supports two types of parameters:

• Positional parameters: ?
• Named parameters: $ followed by an identifier

Functions

A function name is an expression that represent a builtin function. It can either represent a global function or a function within a package.

count()
typeof("hello")
math.atan2(1.1, 1.1)

String functions

Operators

Genji provides a list of operators that can be used to compute operations with expressions. Operators are binary expressions, meaning they always take exactly two operands. It is possible though to combine multiple operators to create an evaluation tree.

Logical operators

Logical operators are operators that return a boolean under certain conditions.

An expression is truthy if it evaluates to a non zero-value of its type.

Comparison operators

These operators are used to compare values and evaluate to a boolean.

Examples:

1 = 1
-> true

1 > 2.5
-> false

3 IN [1, 2, 3]
-> true

5 BETWEEN 2 AND 10
-> true

Conversion during comparison

Prior to comparison, an implicit conversion is operated for the operands to be of the same type. Not all types can be compared together. When two incompatible types are compared, the comparison always returns false, except if one of the operands is NULL, in that case it returns NULL.

Example:

1 > "hello"
-> false

1 < "hello"
-> false

The comparison follows a list of rules that are executed in order:

• If one of the operands is NULL, return NULL.
• If both operands are documents, use the Comparing documents rule
• If both operands are arrays, use the Comparing arrays rule
• If both operands are numbers (INTEGER or DOUBLE), cast the integer to DOUBLE then compare them together.
• If both operands have the same type, compare them together.

In any other case, return false.

Comparing documents

The fields of each document are sorted, then they are compared one by one, until they are found not equal. The comparison is then determined by the result of the comparison between these two values. If both keys are equal, compare the values.

{a: 1, b: 2} = {b: 2, a: 1}
-> true

{} = {}
-> true

{a: 1, b: 3} > {a: 1, b: 2}
-> true

{a: 100} > {aa: 1}
-> false

Comparing arrays

Each elements of both arrays are compared one by one, index by index, until they are found not equal. The comparison is then determined by the result of the comparison between these two values.

[1, 2, 3] > [1, 1 + 1, 1]
-> true

Let’s break down the example above:

1. Index 0: 1 and 1 are equal, the comparison continues
2. Index 1: 2 and 1 + 1 are equal, the comparison continues
3. Index 2: 3 is greater then 1, the comparison stops and the first array is considered greater than the second one

Two empty arrays are considered equal:

[] = []
-> true

The size of arrays doesn’t matter, unless all the elements of the smallest one are equal to the other one. In that case the biggest array is considered greater.

[3] > [1, 100000]
-> true

[1, 2] < [1, 2, 3]
-> true

Arithmetic operators

Arithmetic operations are supported only for the following types:

• INTEGER
• DOUBLE

Note that INTEGER and DOUBLE types can be calculated together, in that case INTEGER values will be converted to DOUBLE prior the operation.

Any usage of these operators on incompatible types will return NULL.

3 + 3.5
-> 6.5

3 + '1'
-> NULL

The case of NULL

Any arithmetic operation with one of the operand being NULL returns NULL.

NULL + 1
-> NULL

5 * 10 - NULL
-> NULL

Division rules

The division obeys a few rules depending on the types of the operands:

• Dividing two integers, always results in an integer
• Dividing by zero, returns NULL

Return type and overflow

The type of the result of an operation doesn’t necessarily match the type of the operands.

• The result of a DOUBLE operation will always return a DOUBLE
• The result of an INTEGER operation will return an INTEGER, unless the return value is bigger than the maximum value of 64-bit integer. In that case, the return type will be a DOUBLE

Other operators

Evaluation tree and precedence

When parsed, an expression is turned into an evaluation tree so it is possible to combine operators to form complex expressions. The order in which these expressions are executed depends on the priority of the operator.

Here is the list of operators ordered by ascending precedence. Operators with higher precedence are executed before the ones with lower precedence

• OR
• AND
• =, !=, <, <=, >, >=
• +, -, |, ^
• *, /, %, &
• ||

Example:

3 + 4 * 2 > 10 AND 2 - 2 = false
-> true

This expression can be represented as the following tree:

.
└── AND
    ├── >
    │   ├── +
    │   │   ├── 3
    │   │   └── *
    │   │       ├── 4
    │   │       └── 2
    │   └── 10
    └── -
        ├── 2
        └── 2

The deepest branches will be executed first, recursively until reaching the root.

3 - Reference

3.1 - ALTER TABLE

Synopsis

ALTER TABLE statement

Field definition

Field constraint

3.2 - CREATE INDEX

Synopsis

CREATE INDEX statement

3.3 - CREATE SEQUENCE

Synopsis

CREATE SEQUENCE statement

3.4 - Transactions

Synopsis

BEGIN TRANSACTION statement

ROLLBACK TRANSACTION statement

COMMIT TRANSACTION statement

3.5 - CREATE TABLE

Synopsis

CREATE TABLE statement

Parameters

IF NOT EXISTS

Do not throw an error if a table with the same name already exists.

table-name

The name of the table to be created.

Field definition

table-name

The name of the table to be created.

Field constraint

Table constraint

3.6 - DELETE

Synopsis

DELETE statement

3.7 - DROP INDEX

Synopsis

DROP INDEX statement

3.8 - DROP SEQUENCE

Synopsis

DROP SEQUENCE statement

3.9 - DROP TABLE

Synopsis

DROP TABLE statement

3.10 - EXPLAIN

Synopsis

EXPLAIN statement

3.11 - INSERT

Synopsis

INSERT statement