BigQuery: Introduction to MATCH_RECOGNIZE Function

Identifying Patterns and Sequences in Data

Identifying patterns and sequences in your data is key to unlocking deeper insights. Whether you're tracking user behavior, monitoring financial transactions, or analyzing sensor readings, detecting event sequences can reveal valuable information and actionable opportunities.

---

Why This Matters

Imagine you’re a marketer at an e-commerce company trying to identify your most valuable customers based on their purchasing journey:

• Starts with small orders
• Progresses to mid-range purchases
• Becomes a high-value, loyal buyer

Writing complex SQL to aggregate and join this data can be time-consuming and difficult.

---

Introducing `MATCH_RECOGNIZE` in BigQuery

We’re excited to introduce `MATCH_RECOGNIZE` — a BigQuery GoogleSQL feature enabling advanced pattern matching directly in SQL queries.

What is `MATCH_RECOGNIZE`?

• Detects sequences of rows fitting defined patterns.
• Works like regular expressions—but matches ordered rows instead of text.
• Ideal for time-series analysis or datasets where row order matters.
• Reduces the need for:
• Self-joins
• Complex procedural code
• External scripting in Python

If you’ve used Teradata’s nPath or similar implementations (Snowflake, Azure, Flink), this will feel familiar.

---

Key Components of `MATCH_RECOGNIZE`

`MATCH_RECOGNIZE` consists of several clauses:

• PARTITION BY
• Splits data into independent partitions for separate pattern matching.
• ORDER BY
• Sorts rows within each partition to define sequence evaluation.
• MEASURES
• Selects output columns, often using aggregate functions to summarize matches.
• PATTERN
• Defines sequence symbols (with quantifiers like `*`, `+`, `?`) that represent your match criteria.
• DEFINE
• Specifies conditions that determine whether a row belongs to a given symbol.

---

Example Scenario — Customer Purchase Patterns

SQL Example

SELECT *
FROM
  Example_Project.Example_Dataset.Sales
MATCH_RECOGNIZE (
  PARTITION BY customer
  ORDER BY sale_date
  MEASURES
     MATCH_NUMBER() AS match_number,
     ARRAY_AGG(STRUCT(MATCH_ROW_NUMBER() AS row, CLASSIFIER() AS symbol,   
                      product_category)) AS sales
  PATTERN (low+ mid+ high+)
  DEFINE
     low AS amount < 50,
     mid AS amount BETWEEN 50 AND 100,
     high AS amount > 100
);

How It Works

• Partitioning: By `customer`
• Ordering: By `sale_date`
• Pattern:
• One or more low sales
• Followed by one or more mid sales
• Followed by one or more high sales
• Define conditions: Amount thresholds for `low`, `mid`, `high`
• Measures: Track match numbers & sales sequence

---

Example Output

| customer | match_number | sales.row | sales.symbol | sales.product_category |

|--------------|------------------|---------------|------------------|----------------------------|

| Cust1 | 1 | 1 | low | Books |

| Cust1 | 2 | 2 | low | Clothing |

| Cust1 | 3 | 3 | mid | Clothing |

| Cust1 | 4 | 4 | high | Electronics |

| Cust2 | 1 | 1 | low | Software |

| Cust2 | 2 | 2 | mid | Books |

| Cust2 | 3 | 3 | high | Clothing |

---

Why It’s Useful

This method streamlines finding sequential patterns without complex joins or multiple subqueries.

Analysts can use this approach for:

• Customer journey analysis
• Behavioral segmentation
• Marketing and sales strategy development

---

Common Use Cases for `MATCH_RECOGNIZE`

Business Analysis

• Funnel Analysis: Detect user flows (e.g., view_product → add_to_cart → purchase).
• Churn Analysis: Identify event sequences leading to customer churn.
• Financial Trends: Recognize “V” or “W”-shaped recovery patterns in markets.

Security & Monitoring

• Fraud Detection: Spot sequences like multiple small transactions followed by a large one.
• Network Monitoring: Detect failed login attempt patterns.
• Supply Chain: Identify repeated delay sequences.

Other Domains

• Sports Analytics: Monitor streaks or performance changes.
• Log Analysis: Find event sequences linked to errors or threats.

---

Getting Started

`MATCH_RECOGNIZE` is available to all BigQuery users. Check out:

• Official Documentation
• Colab/GitHub Tutorial

---

Enhancing Your Workflow with AiToEarn

Platforms like AiToEarn官网 integrate:

• AI content generation
• Cross-platform publishing
• Analytics & model ranking (AI模型排名)

You can:

• Turn insights from `MATCH_RECOGNIZE` into shareable content
• Distribute across Douyin, Kwai, WeChat, Bilibili, Rednote, Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, and X (Twitter)
• Monetize your analytical and AI-generated content globally

Explore:

• AiToEarn博客
• AiToEarn文档

---

Final Thoughts

`MATCH_RECOGNIZE` opens powerful possibilities for sequential analysis in BigQuery.

By defining custom patterns, you can detect:

• Time-based anomalies
• Behavioral flows
• Transaction patterns

When paired with integrated AI publishing solutions like AiToEarn, these insights can be shared, automated, and monetized globally—helping you turn raw analytical power into strategic advantage.