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Table of contents
1.
Introduction
2.
Import and export data
2.1.
Avro files
2.2.
Parquet files
2.3.
SequenceFiles
3.
Migrating Data from HBase to Cloud Bigtable
3.1.
Pre-migration considerations
3.2.
Before starting your migration, this section offers suggestions for evaluating and considering.
3.3.
Performance
3.4.
Bigtable schema design
3.5.
Authentication and authorization
4.
Migrating HBase to Bigtable
4.1.
Stop sending writes to HBase.
4.2.
Take HBase table snapshots
4.3.
Export the HBase snapshots to Cloud Storage
4.4.
Compute and export hashes
4.5.
Create destination tables
5.
Frequently Asked Question
5.1.
Which feature is supported by Cloud Bigtable?
5.2.
What is the difference between BigQuery and Bigtable?
5.3.
Why did Google create Bigtable?
5.4.
What language is Bigtable in?
5.5.
Is the Bigtable column based?
6.
Conclusion
Last Updated: Mar 27, 2024

Basics of Cloud Bigtable with Migration Concept

Author Muskan Sharma
0 upvote
Leveraging ChatGPT - GenAI as a Microsoft Data Expert
Speaker
Prerita Agarwal
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23 Jul, 2024 @ 01:30 PM

Introduction

While working on large data, we have to store the data in tables. So what to do when you have billions of rows and columns? For that, we have Cloud BigTable.

You can store terabytes or even petabytes of data in Cloud Bigtable, a large table containing billions of rows and thousands of columns.

So in this blog, you'll learn the Basics of Cloud Bigtable with Migration Concept

Let’s dive into the topic to explore more.

Import and export data

The techniques for importing and exporting data into and out of Cloud Bigtable are listed on this page.

Avro files

You can export data from Bigtable as Avro files and then import that data back into Bigtable using the following Dataflow templates. The Google Cloud console or the gcloud command-line tool can be used to run the templates.

Cloud Bigtable to Cloud Storage Avro

Bigtable's Cloud Storage Alternative Avro templates are pipelines that read information from Bigtable tables and publish it in Avro format to Cloud Storage buckets. The template can be used to transfer data from Bigtable to cloud storage.

  • Conditions for using this pipeline:
  • There must be a Bigtable table.
  • Before starting the pipeline, the output Cloud Storage bucket must be present.

Cloud Storage Avro to Cloud Bigtable

Avro files in a Cloud Storage bucket are read using the Cloud Storage Avro to Bigtable template, which then writes the data to a Bigtable table. The template can be used to transfer data from Cloud Storage to Bigtable.

  • Conditions for using this pipeline:
  • There must be a Bigtable table with the same column families as those exported in the Avro files.
  • Before starting the pipeline, the input Avro files must be present in a Cloud Storage bucket.
  • Bigtable requires the input Avro files to conform to a certain schema.

Parquet files

You can export data from Bigtable as Parquet files and then import that data back into Bigtable using the following Dataflow templates. The Google Cloud console or the gcloud command-line tool can be used to run the templates.

Cloud Bigtable to Cloud Storage Parquet

Bigtable's Cloud Storage Alternative A pipeline called a parquet template extracts data from a Bigtable table and sends it in parquet format to a Cloud Storage bucket. The template can be used to transfer data from Bigtable to cloud storage.

  • Conditions for using this pipeline:
  • There must be a Bigtable table.
  • Before starting the pipeline, the output Cloud Storage bucket must be present.

Cloud Storage Parquet to Cloud Bigtable

The pipeline used by the Cloud Storage Parquet to Bigtable template receives information from Parquet files in a bucket and publishes it to a Bigtable table. The template can be used to transfer data from Cloud Storage to Bigtable.

  • Conditions for using this pipeline:
  • A Bigtable table must have the same column families as those exported in the Parquet files.
  • Before starting the pipeline, the input Parquet files must be present in a Cloud Storage bucket.
  • From the input Parquet files, Bigtable anticipates a particular schema.

SequenceFiles

The following Dataflow templates allow you to export data from Bigtable as SequenceFiles and then import the data back into Bigtable. You can execute the templates by using the gcloud command-line tool or the Google Cloud console.

Bigtable to Cloud Storage SequenceFile

Bigtable's Cloud Storage Alternative A pipeline called a SequenceFile template reads data from a Bigtable table and outputs it in SequenceFile format to a Cloud Storage bucket. The template can be used to transfer data from Bigtable to Cloud Storage.

  • Conditions for using this pipeline:
  • There must be a Bigtable table.
  • Before starting the pipeline, the output Cloud Storage bucket must be present.

Cloud Storage SequenceFile to Bigtable

A pipeline that reads data from SequenceFiles in a Cloud Storage bucket and writes it to a Bigtable table is the Cloud Storage SequenceFile to Bigtable template. The template can be used to transfer data from Cloud Storage to Bigtable.

  • Conditions for using this pipeline:
  • There must be a Bigtable table.
  • Before starting the pipeline, the input SequenceFiles must be in a Cloud Storage bucket.
  • SequenceFiles for the input must have been exported from HBase or Bigtable.
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Migrating Data from HBase to Cloud Bigtable

This article outlines the factors to take into account and the steps involved in moving data from an Apache HBase cluster to a Google Cloud Cloud Bigtable server.

See Migrating HBase on Google Cloud to Bigtable for information on moving data from an HBase cluster hosted on a Google Cloud service, such as Dataproc or Compute Engine.

Before starting this migration, you should think about the Bigtable feature set, your approach to authentication and authorization, the performance implications, and the Bigtable schema architecture.

Pre-migration considerations

Before starting your migration, this section offers suggestions for evaluating and considering.

Performance

Bigtable offers performance that is remarkably predictable under typical workload conditions. Before transferring your data, be sure you know the variables that affect Bigtable performance.

Bigtable schema design

Most of the time, Bigtable and HBase support the same schema designs. Before migrating your data, check the ideas outlined in Designing your schema if you want to update your schema or if your use case is changing.

Authentication and authorization

Examine the current HBase authentication and authorization procedures before creating Bigtable access control.

You switch your current permission on HBase to IAM because Bigtable supports Identity and Access Management, one of the standard authentication methods offered by Google Cloud. The current Hadoop groups that offer HBase access control techniques can be mapped to various service accounts.

At the project, instance, and table levels, Bigtable lets you manage access. See Access Control for further details.

Migrating HBase to Bigtable

You export an HBase snapshot for each table to Cloud Storage and import the data into Bigtable to move your data from HBase to Bigtable. The subsequent sections provide a detailed explanation of these stages for a single HBase cluster.

  1. Your HBase cluster should no longer receive writes.
  2. Take a snapshot of the tables in the HBase cluster.
  3. The snapshot files should be exported to cloud storage.
  4. generate hashes, then export them to cloud storage.
  5. In Bigtable, create destination tables.
  6. Bigtable should be used to import HBase data from cloud storage.
  7. Check the imported data.
  8. Bigtable is written to by Route.

Stop sending writes to HBase.

Stop sending writes to your HBase cluster before you take snapshots of your HBase tables.

Take HBase table snapshots

Take a snapshot of each table you intend to move to Bigtable once your HBase cluster has stopped accepting new data.

At first, a snapshot uses very little space on the HBase cluster, but over time, it can end up taking up the same amount of space as the original table. There is no CPU resource usage during the snapshot.

Run the following command for each table, giving each snapshot a different name:

echo "snapshot 'TABLE_NAME', 'SNAPSHOT_NAME'" | hbase shell -n

Export the HBase snapshots to Cloud Storage

You must export the snapshots after you make them. When running export jobs on a production HBase cluster, keep an eye on the cluster and other HBase resources to make sure everything is operating as it should.

Run the following commands for each snapshot that you want to export:

hbase org.apache.hadoop.hbase.snapshot.ExportSnapshot \
-Dhbase.zookeeper.quorum=$ZOOKEEPER_QUORUM_AND_PORT -snapshot SNAPSHOT_NAME \
    -copy-from $MIGRATION_SOURCE_DIRECTORY \
    -copy-to $MIGRATION_DESTINATION_DIRECTORY/data

Compute and export hashes

Make hashes next, which will be used for validation after the migration is finished. The HBase validation tool HashTable generates hashes for row ranges and outputs them to files. To verify the integrity of the migrated data and compare the hashes, you can perform a sync-table job on the destination table.

The following command should be run for every table you exported:

hbase org.apache.hadoop.hbase.mapreduce.HashTable --batchsize=32000 --numhashfiles=20 \
TABLE_NAME $MIGRATION_DESTINATION_DIRECTORY/hashtable/TABLE_NAME

Create destination tables

For each snapshot that you exported, the next step is to build a destination table in your Bigtable instance. Utilize a user account with bigtable. tables. provide the instance the ability to create.

This manual uses the Cloud Bigtable Schema Translation tool, which generates the table on your behalf. However, you can construct a table using the cbt command-line tool or the Google Cloud console if you don't want your Bigtable schema to exactly match the HBase model.

The HBase table's schema, including the table name, column families, garbage collection rules, and splits, is captured by the Cloud Bigtable Schema Translation tool. Then it makes a comparable Bigtable table.

Run the following command to replicate the schema from HBase to Bigtable for each table you want to import.

java \
 -Dgoogle.bigtable.project.id=$PROJECT_ID \
 -Dgoogle.bigtable.instance.id=$INSTANCE_ID \
 -Dgoogle.bigtable.table.filter=TABLE_NAME \
 -Dhbase.zookeeper.quorum=$ZOOKEEPER_QUORUM \
 -Dhbase.zookeeper.property.clientPort=$ZOOKEEPER_PORT \
 -jar $TRANSLATE_JAR

Frequently Asked Question

Which feature is supported by Cloud Bigtable?

Bigtable enables high read and write throughput at low latency for quick access to massive volumes of data and is perfect for storing very large amounts of data in a key-value store.

What is the difference between BigQuery and Bigtable?

Bigtable is a wide-column NoSQL database designed for high read and write volumes. For vast amounts of structured relational data, on the other hand, BigQuery functions as an enterprise data warehouse.

Why did Google create Bigtable?

Bigtable was created to enable applications needing tremendous scalability; the technology was meant to be utilized with petabytes of data from the beginning.

What language is Bigtable in?

Go, Python, Java, C++, and Ruby are the languages used in Bigtable.

Is the Bigtable column based?

Since Bigtable is a row-oriented database, all of the data for a single row are stored together before being arranged by column family and then by column.

Conclusion

This blog has extensively discussed the Basics of Cloud Bigtable with Migration Concept Migrating Data from HBase to Cloud Bigtable, etc. We hope this blog has helped you learn about the Migration Concept in Cloud BigTable.

If you want to learn more, check out the excellent content on the Coding Ninjas Website:

Advanced Concepts of Cloud Bigtable with Migration ConceptsOverview of cloud BigtableAsynchronous Data Transfer, and Overview of cloud billing concepts.

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