Bigtable for time-series data in GCP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a Bigtable instance with the correct type for time-series data.

GCP

gcloud bigtable instances create my-instance --cluster=my-cluster --cluster-zone=us-central1-b --display-name=my-instance --instance-type=[1]

Drag options to blanks, or click blank then click option'

ADEVELOPMENT

BTIME_SERIES

CSTANDARD

DPRODUCTION

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing DEVELOPMENT instance type which is for testing only.

Using a non-existent instance type like TIME_SERIES.

The PRODUCTION instance type is used for Bigtable instances that handle real workloads, including time-series data.

2fill in blank

medium

Complete the code to create a Bigtable table with a column family for storing time-series data.

GCP

cbt createtable my-table
cbt createfamily my-table [1]

Drag options to blanks, or click blank then click option'

Ametrics

Bdata

Ctimeseries

Dcf1

Attempts:

3 left

💡 Hint

Common Mistakes

Using generic names like 'data' which are less descriptive.

Using multiple column families unnecessarily.

The column family name metrics is commonly used to store time-series data in Bigtable.

3fill in blank

hard

Fix the error in the row key design for efficient time-series data storage.

GCP

row_key = '[1]#' + timestamp + '#' + sensor_id

Drag options to blanks, or click blank then click option'

Aregion

Btimestamp

Csensor_id

Ddevice

Attempts:

3 left

💡 Hint

Common Mistakes

Using timestamp first causes hotspotting in Bigtable.

Using region or device without context may not distribute data well.

Placing sensor_id first in the row key helps distribute writes evenly across Bigtable nodes, improving performance for time-series data.

4fill in blank

hard

Fill both blanks to create a Bigtable schema snippet that defines a column family and sets a garbage collection rule to keep only the last 7 days of data.

GCP

column_family = bigtable.ColumnFamily(name='metrics', gc_rule=bigtable.GCRule.[1](days=[2]))

Drag options to blanks, or click blank then click option'

Amax_age

Bmax_versions

C7

D30

Attempts:

3 left

💡 Hint

Common Mistakes

Using max_versions instead of max_age for time-based data retention.

Not setting the days parameter correctly.

The max_age garbage collection rule with days=7 keeps only data from the last 7 days.

5fill in blank

hard

Fill all three blanks to write a Python snippet that writes a time-series data point to Bigtable with the correct row key, column family, and column qualifier.

GCP

row_key = '[1]#' + timestamp
row = table.row(row_key)
row.set_cell('[2]', '[3]', value)
row.commit()

Drag options to blanks, or click blank then click option'

Asensor123

Bmetrics

Ctemperature

Dtimestamp

Attempts:

3 left

💡 Hint

Common Mistakes

Using timestamp as the first part of the row key causing hotspots.

Using wrong column family or qualifier names.

The row key starts with the sensor ID sensor123. The column family is metrics, and the column qualifier is temperature to store the temperature value.

Practice

1. What is the main reason to use Google Cloud Bigtable for time-series data?

easy

A. It provides built-in machine learning models for time-series forecasting.

B. It automatically creates visual dashboards for time-series data.

C. It can handle large volumes of data with fast read and write speeds.

D. It stores data only in JSON format for easy access.

Solution

1. Step 1: Understand Bigtable's design for data volume

   Bigtable is designed to store very large amounts of data efficiently, especially for time-series data which grows quickly.

2. Step 2: Recognize Bigtable's performance benefits

   It offers fast read and write speeds, which is essential for time-series data that updates frequently.

3. Final Answer:

   It can handle large volumes of data with fast read and write speeds. -> Option C

4. Quick Check:

   Bigtable = fast, large data [OK]

Hint: Bigtable excels at fast, large-scale data storage [OK]

Common Mistakes:

• Confusing Bigtable with visualization tools
• Assuming Bigtable has built-in ML models
• Thinking Bigtable only stores JSON

2. Which of the following is the correct way to define a Bigtable instance using gcloud CLI for time-series data?

easy

A. gcloud bigtable instance create my-instance --cluster=my-cluster --zone=us-central1-b

B. gcloud bigtable create instance my-instance --zone=us-central1-b --type=DEVELOPMENT

C. gcloud bigtable instances new my-instance --cluster-zone=us-central1-b --instance-type=PRODUCTION

D. gcloud bigtable instances create my-instance --cluster=my-cluster --cluster-zone=us-central1-b --display-name="My Instance" --instance-type=PRODUCTION

Solution

1. Step 1: Review gcloud CLI syntax for Bigtable instance creation

   The correct command uses 'gcloud bigtable instances create' with required flags for cluster, zone, display name, and instance type.

2. Step 2: Compare options with correct syntax

   gcloud bigtable instances create my-instance --cluster=my-cluster --cluster-zone=us-central1-b --display-name="My Instance" --instance-type=PRODUCTION matches the official syntax exactly, including cluster and zone flags.

3. Final Answer:

   gcloud bigtable instances create my-instance --cluster=my-cluster --cluster-zone=us-central1-b --display-name="My Instance" --instance-type=PRODUCTION -> Option D

4. Quick Check:

   Correct gcloud syntax = gcloud bigtable instances create my-instance --cluster=my-cluster --cluster-zone=us-central1-b --display-name="My Instance" --instance-type=PRODUCTION [OK]

Hint: Use 'gcloud bigtable instances create' with cluster and zone [OK]

Common Mistakes:

• Using incorrect command verbs like 'create instance' or 'instances new'
• Missing required flags like --cluster-zone
• Confusing instance type flags

3. Given the following Bigtable row key design for time-series data: deviceID#timestamp, what will be the order of rows when scanned?

medium

A. Rows are ordered by deviceID first, then by timestamp ascending.

B. Rows are ordered by timestamp first, then by deviceID ascending.

C. Rows are unordered and returned randomly.

D. Rows are ordered by timestamp descending, then deviceID.

Solution

1. Step 1: Understand Bigtable row key sorting

   Bigtable sorts rows lexicographically by row key from left to right.

2. Step 2: Analyze the key format deviceID#timestamp

   Since deviceID is first, rows are grouped by deviceID, then sorted by timestamp ascending within each device.

3. Final Answer:

   Rows are ordered by deviceID first, then by timestamp ascending. -> Option A

4. Quick Check:

   Row key order = deviceID then timestamp [OK]

Hint: Row keys sort left to right lex order [OK]

Common Mistakes:

• Assuming timestamp sorts first
• Thinking rows are unordered
• Confusing ascending vs descending order

4. You notice your Bigtable time-series data query is slow. Which of the following is a likely cause and fix?

medium

A. Cause: Using multiple clusters. Fix: Use a single cluster only.

B. Cause: Row keys are timestamp first causing hotspotting. Fix: Reverse the key to deviceID#timestamp.

C. Cause: Too many column families. Fix: Add more column families.

D. Cause: Data stored in JSON format. Fix: Convert data to XML.

Solution

1. Step 1: Identify hotspotting from row key design

   If timestamp is first in the key, writes concentrate on a small range causing slow queries.

2. Step 2: Fix by changing row key order

   Placing deviceID first distributes writes and reads better, improving performance.

3. Final Answer:

   Cause: Row keys are timestamp first causing hotspotting. Fix: Reverse the key to deviceID#timestamp. -> Option B

4. Quick Check:

   Hotspotting fix = reorder row key [OK]

Hint: Avoid timestamp-first keys to prevent hotspots [OK]

Common Mistakes:

• Thinking multiple clusters slow queries
• Adding column families improves speed
• Confusing data format impact on speed

5. You want to design a Bigtable schema for IoT sensor data that arrives every second from thousands of devices. Which approach best supports efficient queries for recent data per device?

hard

A. Use row keys as deviceID#reversed-timestamp to keep recent data first and group by device.

B. Use row keys as timestamp#deviceID to sort by time globally.

C. Store all data in a single row per device with columns for each timestamp.

D. Use random UUIDs as row keys to distribute load evenly.

Solution

1. Step 1: Consider query pattern for recent data per device

   Queries often request recent data, so sorting recent timestamps first helps.

2. Step 2: Use reversed timestamp in row key

   Reversing timestamp (e.g., max - timestamp) sorts recent data at the top for fast access.

3. Step 3: Group by deviceID first

   This groups data by device, making queries per device efficient.

4. Final Answer:

   Use row keys as deviceID#reversed-timestamp to keep recent data first and group by device. -> Option A

5. Quick Check:

   Reversed timestamp + deviceID = fast recent queries [OK]

Hint: Reverse timestamp in key for recent-first order [OK]

Common Mistakes:

• Using timestamp first causes hotspots
• Storing all data in one row causes size limits
• Random UUID keys prevent efficient range scans