SQL Query Reference

Eventflux uses an extended SQL syntax designed for stream processing. This reference covers all supported query constructs.

Stream Definitions

DEFINE STREAM

Create a new stream with a schema:

DEFINE STREAM StreamName (
    attribute1 TYPE,
    attribute2 TYPE,
    ...
);

Supported Types:

Type	Description	Example
INT	32-bit integer	42
LONG	64-bit integer	9223372036854775807
FLOAT	32-bit floating point	3.14
DOUBLE	64-bit floating point	3.14159265359
STRING	UTF-8 text	'hello'
BOOL	Boolean	true, false

Example:

DEFINE STREAM StockTrades (
    symbol STRING,
    price DOUBLE,
    volume INT,
    timestamp LONG
);

DEFINE TABLE

Create a table for storing and querying reference data:

DEFINE TABLE TableName (
    attribute1 TYPE,
    attribute2 TYPE,
    ...
);

Tables can be joined with streams for enrichment:

DEFINE TABLE StockInfo (
    symbol STRING,
    company_name STRING,
    sector STRING
);

SELECT t.symbol, t.price, s.company_name
FROM Trades AS t
JOIN StockInfo AS s
  ON t.symbol = s.symbol
INSERT INTO EnrichedTrades;

Basic Queries

SELECT Statement

SELECT attribute1, attribute2, expression AS alias
FROM StreamName
WHERE condition
INSERT INTO OutputStream;

Example:

SELECT symbol, price, volume * price AS total_value
FROM StockTrades
WHERE price > 100.0
INSERT INTO HighValueTrades;

SELECT with Aliases

SELECT
    s.symbol,
    s.price AS trade_price,
    s.volume * s.price AS notional
FROM StockTrades AS s
INSERT INTO ProcessedTrades;

Operators

Arithmetic

Operator	Description	Example
+	Addition	price + fee
-	Subtraction	high - low
*	Multiplication	price * volume
/	Division	total / count
%	Modulo	id % 10

Comparison

Operator	Description	Example
=	Equal	symbol = 'AAPL'
!=, <>	Not equal	status != 'CLOSED'
<	Less than	price < 100
>	Greater than	volume > 1000
<=	Less than or equal	temp <= 0
>=	Greater than or equal	count >= 5

Logical

Operator	Description	Example
AND	Logical AND	a > 0 AND b > 0
OR	Logical OR	status = 'A' OR status = 'B'
NOT	Logical NOT	NOT is_deleted
IS NULL	Null check	email IS NULL
IS NOT NULL	Not null check	name IS NOT NULL

String

Operator	Description	Example
LIKE	Pattern match	name LIKE 'J%'
IN	Set membership	status IN ('A', 'B')
BETWEEN	Range check	price BETWEEN 10 AND 100

Expressions

Mathematical Functions

SELECT
    ABS(price_change) AS abs_change,
    SQRT(variance) AS std_dev,
    POWER(growth_rate, 2) AS squared_growth,
    ROUND(price, 2) AS rounded_price
FROM DataStream
INSERT INTO Calculations;

String Functions

SELECT
    UPPER(symbol) AS upper_symbol,
    LOWER(name) AS lower_name,
    LENGTH(description) AS desc_length,
    CONCAT(first_name, ' ', last_name) AS full_name
FROM DataStream
INSERT INTO Processed;

Conditional Expressions

SELECT
    symbol,
    CASE
        WHEN price > 100 THEN 'HIGH'
        WHEN price > 50 THEN 'MEDIUM'
        ELSE 'LOW'
    END AS price_tier
FROM StockTrades
INSERT INTO Categorized;

COALESCE and NULLIF

SELECT
    COALESCE(nickname, name, 'Unknown') AS display_name,
    NULLIF(status, 'UNKNOWN') AS valid_status
FROM Users
INSERT INTO ProcessedUsers;

INSERT INTO

All queries must specify an output destination:

-- Insert into a stream
SELECT * FROM Input INSERT INTO OutputStream;

-- The output stream is automatically created if not defined

Multiple Queries

An EventFlux application can contain multiple queries:

DEFINE STREAM RawTrades (symbol STRING, price DOUBLE, volume INT);

-- Query 1: Filter high-value trades
SELECT symbol, price, volume
FROM RawTrades
WHERE price * volume > 10000
INSERT INTO HighValueTrades;

-- Query 2: Compute statistics
SELECT symbol, AVG(price) AS avg_price
FROM RawTrades
WINDOW TUMBLING(1 min)
GROUP BY symbol
INSERT INTO TradeStats;

-- Query 3: Alert on anomalies
SELECT symbol, avg_price
FROM TradeStats
WHERE avg_price > 500
INSERT INTO PriceAlerts;

Comments

-- This is a single-line comment

/* This is a
   multi-line comment */

DEFINE STREAM Input (
    value INT  -- inline comment
);

Best Practices

Query Design Tips

1. Filter early - Apply WHERE clauses as close to the source as possible
2. Use appropriate windows - Choose window type based on your use case
3. Avoid SELECT * - Explicitly list needed columns for better performance
4. Name your outputs - Use meaningful INSERT INTO targets for clarity

Next Steps

• Windows - Time and count-based windowing
• Aggregations - GROUP BY and aggregate functions
• Joins - Stream-to-stream and stream-to-table joins
• Patterns - Complex event pattern detection
• Functions - Built-in function reference