Understanding BigQuery’s Filtering for Date Ranges

As a technical blogger, I’ve encountered numerous questions from users who struggle to extract specific data from their datasets using BigQuery’s SQL-like language, BigQuery Query Language (BQL). One common challenge is finding new data within a specified date range. In this article, we’ll delve into the world of BigQuery filtering for date ranges and explore ways to achieve the desired results.

Introduction to BigQuery

BigQuery is a fully-managed enterprise data warehouse service by Google Cloud Platform. It provides an efficient way to process and analyze large datasets using SQL-like queries. With BigQuery, users can easily retrieve insights from their data without having to manage infrastructure or worry about storage costs.

Understanding the Problem Statement

The question at hand involves finding “new” users who used a road (specifically mentioned as “Road”) within a given date range. The goal is to group this result by the road name, providing counts of total users and new users.

Breaking Down the Query

To achieve this, we need to break down the query into several components:

1. Data Ingestion: First, we must ensure that our data is properly ingested into BigQuery.
2. Filtering by Date Range: We’ll use BigQuery’s date filtering capabilities to extract data within the desired range.
3. Identifying “New” Users: To identify new users, we need to assign a unique sequence number (seqnum) to each user based on their timestamp and road type.
4. Grouping Results by Road: Finally, we’ll group our results by road name, counting the total number of users and identifying which ones are “new.”

Data Ingestion

Before diving into filtering and grouping data, it’s crucial to ensure that your data is properly ingested into BigQuery.

To do this, you can use the LOAD DATA statement or upload your data directly from a CSV file. For example:

LOAD DATA FROM '@file:///data.csv'
 OPTIONS (skip_header = 1)
 INTO TABLE my_table;

Replace '@file:///data.csv' with the actual path to your dataset.

Filtering by Date Range

To filter data within a specific date range, you can use BigQuery’s date filtering capabilities. Let’s consider our query:

SELECT road, count(distinct user_id) as total_users,
       count(CASE WHEN seqnum = 1 THEN 1 END) OVER (PARTITION BY road ORDER BY timestamp) as new_users
FROM (
  SELECT l.*, row_number() over (partition by l.user_id, l.text order by l.timestamp) as seqnum
  FROM log l
  WHERE l.type = 'Road'
)
WHERE timestamp >= @timestamp1 AND timestamp < @timestamp2
GROUP BY road;

In this query:

• @timestamp1 and @timestamp2 represent the start and end dates of our filter range.
• The subquery assigns a unique sequence number (seqnum) to each user based on their timestamp and road type.

Identifying “New” Users

To identify new users, we’ll count the occurrences of seqnum = 1 for each road. This is achieved using BigQuery’s COUNT(CASE WHEN ... THEN 1 END) statement.

SELECT road, count(distinct user_id) as total_users,
       COUNT(CASE WHEN seqnum = 1 THEN 1 END) OVER (PARTITION BY road ORDER BY timestamp) as new_users
FROM (
  SELECT l.*, row_number() over (partition by l.user_id, l.text order by l.timestamp) as seqnum
  FROM log l
  WHERE l.type = 'Road'
)
WHERE timestamp >= @timestamp1 AND timestamp < @timestamp2
GROUP BY road;

Grouping Results by Road

Finally, we’ll group our results by road name and count the total number of users and new users.

SELECT road, count(distinct user_id) as total_users,
       COUNT(CASE WHEN seqnum = 1 THEN 1 END) OVER (PARTITION BY road ORDER BY timestamp) as new_users
FROM (
  SELECT l.*, row_number() over (partition by l.user_id, l.text order by l.timestamp) as seqnum
  FROM log l
  WHERE l.type = 'Road'
)
WHERE timestamp >= @timestamp1 AND timestamp < @timestamp2
GROUP BY road;

Conclusion

In this article, we’ve explored ways to find “new” users who used a road within a specified date range in BigQuery. By breaking down the query into several components and leveraging BigQuery’s filtering capabilities, we can efficiently retrieve insights from our dataset.

With this knowledge, you’ll be able to tackle similar challenges with confidence and extract valuable data from your datasets using BigQuery’s powerful features.

Last modified on 2025-01-29