Google Cloud allows you to move your PostgreSQL databases to Cloud SQL with Database Migration Service (DMS). DMS gives you the ability to replicate data continuously to the destination database, while the source is live in production, enabling you to migrate with minimum downtime.

However, terabyte-scale migrations can be complex. For instance, if your PostgreSQL database has Large Objects, then you will require some downtime to migrate them manually as that is a limitation of DMS. There are few more such limitations – check out known limitations of DMS. If not handled carefully, these steps can extend the downtime during cutover, lead to performance impact on the source instance, or even delay the project delivery date. All this may mean significant business impact.

Searce is a technology consulting company, specializing in modernizing application and database infrastructure by leveraging cloud, data and AI. We empower our clients to accelerate towards the future of their business. In our journey, we have helped dozens of clients migrate to Cloud SQL, and have found terabyte-scale migrations to be the toughest for the reasons mentioned earlier.

This blog centers around our work in supporting an enterprise client whose objective was to migrate dozens of terabyte scale, mission-critical PostgreSQL databases to Cloud SQL with minimum downtime. Their largest database was 20TB in size and all the databases had tables with large objects and some tables did not have primary keys. Note that DMS had a limitation of not supporting migration of tables without a primary key during the time of this project. In June 2022, DMS released an enhancement to support the migration of tables without a primary key.

In this blog, we share with you our learnings about how we simplified and optimized this migration, so that you can incorporate our best practices into your own migrations. We explore mechanisms to reduce the downtime required for operations not handled by DMS by ~98% with the use of automation scripts. We also explore database flags in PostgreSQL to optimize DMS performance and minimize the overall migration time by ~15%.

Optimize DMS performance with database flags

Once the customer made the decision to migrate PostgreSQL databases to Google Cloud SQL, we considered two key factors that would decide business impact – migration effort and migration time. To minimize effort for the migration of PostgreSQL databases, we leveraged Google Cloud’s DMS (Database Migration Service) as it is very easy to use and it does the heavy lifting by continuously replicating data from the source database to the destination Cloud SQL instance, while the source database is live in production.

How about migration time? For a terabyte-scale database, depending on the database structure, migration time can be considerably longer. Historically, we observed that DMS took around 3 hours to migrate a 1 TB database. In other cases, where the customer database structure was more complex, migration took longer. Thankfully, DMS takes care of this replication while the source database is live in production, so no downtime is required during this time. Nevertheless, our client would have to bear the cost of both the source and destination databases which for large databases, might be substantial. Meanwhile, if the database size increased, then replication could take even longer, increasing the risk of missing the customer’s maintenance window for the downtime incurred during cutover operations. Since the customer’s maintenance window was monthly, we would have to wait for 30 more days for the next maintenance window, requiring the customer to bear the cost of both the databases for another 30 days. Furthermore, from a risk management standpoint, the longer the migration timeframe, the greater the risk that something could go wrong. Hence, we started exploring options to reduce the migration time. Even the slightest reduction in migration time could significantly reduce the cost and risk.

We explored options around tuning PostgreSQL’s database flags on the source database. While DMS has its own set of prerequisite flags for the source instance and database, we also found that flags like shared_buffers, wal_buffers and maintenance_work_mem helped accelerate the replication process through DMS. These flags needed to be set to a specific value to get the maximum benefit out of each of them. Once set, their cumulative impact was a reduction in time for DMS to replicate a 1 TB database by 4 hours, that is, reduction of 3.5 days for a 20 TB database. Let’s dive into each of them.

Shared Buffers

PostgreSQL uses two buffers – its own internal buffer and the kernel buffered IO. In other words, that data is stored in memory twice. The internal buffer is called shared_buffers, and it determines the amount of memory used by the database for the operating system cache. By default this value is set conservatively low. However, increasing this value on the source database to fit our use case helped increase the performance of read heavy operations, which is exactly what DMS does once a job has been initialized.

After multiple iterations, we found that if the value was set to 55% of the database instance RAM, it boosted the replication performance (a read heavy operation) by a considerable amount and in turn reduced the time required to replicate the data.

WAL Buffers

PostgreSQL relies on Write-Ahead Logging (WAL) to ensure data integrity. WAL records are written to buffers and then flushed to disk. The flag wal_buffers, determines the amount of shared memory used for WAL data that has not yet been written to disk – records that are yet to be flushed. We found that increasing the value for wal_buffers from the default value of 16MB to about 3% of the database instance’s RAM significantly improved the write performance by writing fewer but larger files to the disk at each transaction commit.

Maintenance Work Mem

PostgreSQL maintenance operations, such as VACUUM, CREATE INDEX, and ALTER TABLE ADD FOREIGN KEY, consume their own specific memory. This memory is referred to as maintenance_work_mem. Unlike other operations, PostgreSQL maintenance operations can only be performed sequentially by the database. Setting a value significantly higher than the default value of 64 MB meant that no maintenance operation would block the DMS job. We found that maintenance_work_mem worked best at the value of 1 GB.

Resize source instance to avoid performance impact

Each of these three flags tune how PostgreSQL utilizes memory resources. Hence, it was imperative that before setting these flags, we needed to upsize the source database instance to accommodate them. Without upsizing the database instances, we could have caused application performance degradation, as more than half of the total database memory would be allocated to the processes managed by these flags.

We calculated the memory required by the flags mentioned above, and found that each flag needed to be set to a specific percentage of the source instance’s memory, irrespective of the existing values that might be set for the flags:

• shared_buffers: 55% of source instance’s memory
• wal_buffers: 3% of source instance’s memory
• maintenance_work_mem: 1 GB

We added the individual memory requirements by the flags, and found that 58% of the RAM at least will be taken up by these memory flags. For example, if a source instance used 100GB of memory, 58GB would be taken up by shared_buffers and wal_buffers, and an additional 1GB by maintenance_work_mem. As the original value of these flags was very low (~200MB), we upsized the RAM of the source database instance by 60% in order to ensure that the migration did not impact source performance on the application live in production.

Avoid connection error with WAL sender timeout flag

While using Google Cloud’s DMS, if the connection is terminated between DMS and the Cloud SQL instance during the ‘Full Dump in Progress’ phase of the DMS job, the DMS job fails and needs to be reinitiated. Encountering timeouts, especially while migrating a terabyte-scale database, would mean multiple days’ worth of migration being lost and a delay in the cutover plan. For example, if the connection of the DMS job for a 20TB database migration is lost after 10 days, the DMS job will have to be restarted from the beginning, leading to 10 days’ worth of migration effort being lost.

Adjusting the WAL sender timeout flag (wal_sender_timeout) helped us avoid terminating replication connections that were inactive for a long time during the full dump phase. The default value for this flag is 60 seconds. To avoid these connections from terminating, and to avoid such high impact failures, we set the value of this flag to 0 for the duration of database migration. This would avoid connections getting terminated and allowed for smoother replication through the DMS jobs.

Generally, for all the database flags we talked about here, we advised our customer to restore the default flag values once the migration completed.

Reduce downtime required for DMS limitations by automation

While DMS does the majority of database migration through continuous replication when the source database instance is live in production, DMS has certain migration limitations that cannot be addressed when the database is live. For PostgreSQL, the known limitations of DMS include:

1. Any new tables created on the source PostgreSQL database after the DMS job has been initialized are not replicated to the destination PostgreSQL database.
2. Tables without primary keys on the source PostgreSQL database are not migrated. For those tables, DMS migrated only the schema. This is no longer a limitation after the June 2022 product update.
3. The large object (LOB) data type is not supported by DMS.
4. Only the schema for Materialized Views is migrated; the data is not migrated.
5. All data migrated is created under the ownership of cloudsqlexternalsync.

We had to address these aspects of the database migration manually. Since our client’s database had data with the large object data type, tables without primary keys, and frequently changing table structures that cannot be migrated by DMS, we had to manually export and import that data after DMS did most of the rest of the data migration. This part of database migration required downtime to avoid data loss. For a terabyte-scale database, this data can be in the hundreds of GBs, which means higher migration time and hence higher downtime. Furthermore, when you have dozens of databases to migrate, it can be stressful and error-prone for a human to perform these operations while on the clock during the cutover window!

This is where automation helped save the day! Automating the migration operations during the downtime period not only reduced the manual effort and error risk, but also provided a scalable solution that could be leveraged for the migration of 100s of PostgreSQL database instances to Cloud SQL. Furthermore, by leveraging multiprocessing and multithreading, we were able to reduce the total migration downtime for 100s of GBs of data by 98%, thereby reducing the business impact for our client.

How do we get there?

We laid out all the steps that need to be executed during the downtime – that is, after the DMS job has completed its replication from source to destination and before cutting over the application to the migrated database. You can see a chart mapping out the sequence of operations that are performed during the downtime period in Fig 1.

By automating all the downtime operations in this sequential approach, we observed that it took 13 hours for the entire downtime flow to execute for a 1 TB database. This included the migration of 250 MB in new tables, 60 GB in tables without primary keys and 150 GB in large objects.

One key observation we made was that, out of all the steps, only three steps took most of the time: migrating new tables, migrating tables without primary keys, and migrating large objects. These took the longest time because they all required dump and restore operations for their respective tables. However, these three steps did not have a hard dependency on each other as they individually targeted different tables. So we tried to run them in parallel as you can see in Fig 2. But the steps following them – ‘Refresh Materialized View’ and ‘Recover Ownership’ – had to be performed sequentially as they targeted the entire database.

However, running these three steps in parallel required upsizing the Cloud SQL instances, as we wanted to have sufficient resources available for each step. This led us to increase the Cloud SQL instances’ vCPU by 50% and memory by 40%, since the export and import operations depended heavily on vCPU consumption as opposed to memory consumption.

Migrating the new tables (created after the DMS job was initiated) and tables without primary keys was straightforward as we were able to leverage the native utilities offered by PostgreSQL – pg_dump and pg_restore. Both utilities process tables in parallel by using multiple threads– the higher the table count, the higher the number of threads that could be executed in parallel, allowing faster migration. With this revised approach, for the same 1 TB database, it still took 12.5 hours for the entire downtime flow to execute.

This improvement reduced the cutover downtime, but we still found that we needed a 12.5 hour window to complete all the steps. We then discovered that 99% of the time of downtime was taken up by just one step: exporting and importing 150 GB of large objects. It turned out that multiple threads could not be used to accelerate the dump and restore large objects in PostgreSQL. Hence, migrating the large objects single handedly extended the downtime for migration by hours. Fortunately, we were able to come up with a workaround for that.

Optimize migration of Large Object from PostgreSQL database

PostgreSQL contains a large objects facility that provides stream-style access to data stored in a special large-object structure. When large objects are stored, they are broken down into multiple chunks and stored in different rows of the database, but are connected under a single Object Identifier (OID). This OID can thus be used to access any stored Large Object. Although users can add large objects to any table in the database, under the hood, PostgreSQL physically stores all large objects within a database in a single table called pg_largeobjects.

While leveraging pg_dump and pg_restore for export and import of large objects, this single table – pg_largeobject, becomes a bottleneck as the PostgreSQL utilities cannot execute multiple threads for parallel processing, since it’s just one table. Typically, the order of operations for these utilities looks something like this:

1. pg_dump reads the data to be exported from the source database
2. pg_dump writes that data into the memory of the client where pg_dump is being executed
3. pg_dump writes from memory to the disk of the the client (a second write operation)
4. pg_restore reads the data from the client’s disk
5. pg_restore writes the data to the destination database

Normally, these utilities would need to be executed sequentially to avoid data loss or data corruption due to conflicting processes. This leads to further increase in migration time for large objects.

Our workaround for this single-threaded process involved two elements. First, with our solution, we eliminated the second write operation – write from memory to disk (point #3). Instead, once the data was read and written into memory, our program would begin the import process and write data to the destination database. Second, since pg_dump and pg_restore could not use multiple threads to process the large objects in just the pg_largeobjects table, we took it upon ourselves to develop a solution that could use multiple threads. The thread count was based on the number of OIDs in the table – pg_largeobjects, and break that single table into smaller chunks for parallel execution.

This approach brought down Large Object migration operation from hours to minutes, therefore bringing down the downtime needed for all operations to be completed that DMS cannot handle, for the same 1 TB database, from 13 hours to just 18 minutes. A reduction of ~98% in the required downtime.

Conclusion

After multiple optimizations and dry runs, we were able to develop a procedure for our client to migrate dozens of terabyte-scale PostgreSQL databases to Google Cloud SQL with a minimal business impact. We developed practices to optimize DMS-based migration by 15% using database flags and reduce downtime by 98% with the help of automation and innovation. These practices can be leveraged for any terabyte-scale migration of PostgreSQL databases to Google Cloud SQL to accelerate migration, minimize downtime and avoid performance impact on mission critical applications.

At Redisconf 2018 in San Francisco last month, we announced the public beta of Cloud Memorystore for Redis, a fully-managed in-memory data store service. Today, the public beta is available for everyone to try. Cloud Memorystore provides a scalable, more secure and highly available Redis service fully managed by Google. It’s fully compatible with open source Redis, letting you migrate your applications to Google Cloud Platform (GCP) with zero code changes.

As more and more applications need to process data in real-time, you may want a caching layer in your infrastructure to reduce latency for your applications. Redis delivers fast in-memory caching, support for powerful data structures and features like persistence, replication and pub-sub. For example, data structures like sorted sets make it easy to maintain counters and are widely used to implement gaming leaderboards. Whether it’s simple session caching, developing games played by millions of users or building fast analytical pipelines, developers want to leverage the power of Redis without having to worry about VMs, patches, upgrades, firewall rules, etc.

Early adopters of Cloud Memorystore have been using the service for the last few months and they are thrilled with the service.

Simple and flexible provisioning
How you choose to deploy Cloud Memorystore for Redis depends on the availability and performance needs of your application. You can deploy Redis as a standalone instance or with a replica to provide high availability. But while replicating a Redis instance provides only data redundancy, you still need to do the heavy lifting of health checking, electing of a primary, client connections on failover, etc. The Cloud Memorystore service takes away all this complexity and makes it easy for you deploy a Redis instance that meets your application’s needs.

Cloud Memorystore provides two tiers of service, Basic and Standard, each with different availability characteristics. Regardless of the tier of service, you can provision a Redis instance as small as 1 GB up to 300 GB. With network throughput up to 12 Gbps, Cloud Memorystore supports applications with very high bandwidth needs.

Here is a summary of the capabilities of each tier:

Provisioning a Cloud Memorystore instance is simple: just choose a tier, the size you need to support the instance availability and performance needs, and the region. Your Redis instance will be up and running within a few minutes.

“Lift and shift” applications
Once provisioned, using Cloud Memorystore is a breeze. You can connect to the Redis instance using any of the tools and libraries you commonly use in your environment. Cloud Memorystore clients makes use of IP addresses to connect to the instance. Applications always connect to one IP address and Cloud Memorystore ensures the traffic is directed to the primary in case there is a failover.

Other key features
Whether it’s provisioning, monitoring or scaling memory, Cloud Memorystore simplifies common management tasks.

Security
Open-source Redis has very minimal security, and as a developer or administrator, it can be challenging to ensure all Redis instances in your organization are protected. With Cloud Memorystore, Redis instances are deployed using a private IP address, which prevents the instance from being accessed from the internet. You can also use Cloud Identity & Access Management (IAM) roles to ensure granular access for managing the instance. Additionally, authorized networks ensure that the Redis instance is accessible only when connected to the authorized VPC network.

Stackdriver integration
Cloud Memorystore instances publish all the key metrics into Stackdriver, Google Cloud’s monitoring and management suite. You can monitor all of your instances from the Stackdriver dashboard, and use Stackdriver Logging to get more insights about the Redis instances

Seamless memory scaling
When a mobile application goes viral, it may be necessary to provision a larger Redis instance to meet latency and throughput needs. With Cloud Memorystore you can scale up the instance with a few clicks, and the Standard High Availability tier lets you scale the instance with minimal disruption to the application.

On-demand pricing
Cloud Memorystore provides on-demand pricing with no upfront cost and has per second billing. Moreover, there is no charge for network traffic coming in and out of a Cloud Memorystore instance. For more information, refer to Cloud Memorystore pricing.

Coming soon to Cloud Memorystore
This Cloud Memorystore public beta release is just a starting point for us. Here is a preview of some of the features that are coming soon.

• New region support
• Import-Export of RDB files into a Google Cloud Storage bucket
• Support for Redis 4.0
• Support for Redis Cluster

We are excited about what is upcoming for Cloud Memorystore and we would love to hear your feedback! If you have any requests or suggestions, please let us know through Issue Tracker. You can also join the conversation at Cloud Memorystore discussion group.

Sign up for a $300 credit to try Cloud Memorystore and the rest of GCP. Start with a small Redis instance for testing and development, and then when you’re ready, scale up to serve performance-intensive applications.

Want to learn more? Register for the upcoming webinar on Tuesday, June 26th 9:00 am PT to hear all about Cloud Memorystore for Redis.

Oden Technologies is using the Internet of Things (IoT) to improve the factories of today. The giant network of “things” (including people) connected to each other via the Internet has the potential to reduce waste, increase efficiency, and improve safety across all walks of life. Oden is leading IoT innovation in manufacturing by combining wireless connectivity, big data, and cloud computing.

The use of data to improve manufacturing is practically as old as manufacturing itself. But the computerization of manufacturing has resulted in broad and rapid changes to the way data is collected and processed, as well as the sheer volume of data available.

Oden’s goal is to help manufacturers tap into this data to quickly identify process trends and even warning signs of machine breakdown. Such visibility can reveal opportunities to improve manufacturing and maintenance processes that reduce waste and increase profit margins.

Oden designs and develops data collection devices that can plug into almost any kind of machine and can wirelessly transmit data with minimal complexity and setup time.

Once devices are installed, the Oden technology platform processes data to give manufacturers cutting-edge analytics that are easy to comprehend. Analysis produced by the platform provides factory engineers with data points such as detailed root-cause analysis down to the second, factory-wide performance in real-time, and trend analysis.

Improving cloud delivery

Oden’s previous cloud platform performed satisfactorily, but the company evaluated alternatives in search of potential reductions in cost and complexity and increases in performance.

When evaluating Google Cloud Platform, Oden discovered it would require fewer virtual machine (VM) instances for equivalent performance, which would cut costs. Furthermore, Oden could gain more sophisticated data analytics and machine learning capabilities compared with its existing cloud provider.

Today, Oden runs its entire platform on Google Cloud Platform including Google Compute Engine, Google Cloud Pub/Sub, Google Cloud Bigtable, Google Stackdriver, and Google Kubernetes Engine.

“In order to serve our customers, we need a cloud platform that can scale reliably while keeping costs low, perform under heavy loads, and consistently deliver sophisticated features such as machine learning,” says Willem Sundblad, CEO and Founder at Oden Technologies. “Google Cloud Platform is way ahead in all of these areas compared to our previous cloud provider.”

Capturing tens of millions of metrics a day

Using Google Cloud Platform, Oden can help an average factory capture and store approximately 10 million metrics on a single manufacturing line every day.

Metrics can include extremely granular detail, such as the amount of electricity going to machines, the amount of raw material consumed, and the volume of material produced. Sensors can also capture and transmit environmental information such as temperature, humidity, and dew point so that manufacturers can identify weather-related and seasonal impacts on production.

The updated Oden Cloud Platform uses Kubernetes Engine—powered by the open source Kubernetes system—to run application program interfaces (APIs) that capture data from Oden’s wireless devices on the factory floor.

Google Cloud Pub/Sub then sends the data in real time to Google Cloud Bigtable, where data is processed using Oden’s proprietary analytics tools. Google Stackdriver supports Google Cloud Platform monitoring, logging, and diagnostics, which help Oden deliver its cloud platform with confidence.

Oden Technologies builds dashboards powered by Kubernetes Engine, which pull analyzed data from Google Cloud Bigtable. The dashboards provide customers with real-time visibility into their manufacturing lines. Oden Factory Cloud dashboards allow customers to delve deeper into their data to fine-tune production processes or discover the root causes of production issues.

With the previous cloud provider, Oden required 80 VM instances to run the dashboards. With Google Cloud Platform that number has been cut to 45, which dramatically reduces costs and complexity.

“We migrated from our previous cloud provider to Google Cloud Platform in just one month,” says Willem. “Further, our storage and data analytics costs have decreased by 30%. Cost savings like these allow us to protect customers from rising expenses, keeping us focused on bringing the best products possible to market.”

Faster data access; more efficient factories

With Google Cloud Platform, Oden can now deliver a complete factory analytics picture to manufacturers. In environments where thousands of variables affect the bottom line, businesses can now automatically and perpetually record machine and performance measurement. Oden Factory Cloud gives customers access to comprehensive data insights and can eliminate reliance on onsite infrastructure investments to run their own analytics.

Because manufacturers have access to live data and can analyze production data quickly, they can troubleshoot and resolve problems in minutes rather than months. Such information helps improve product quality, minimize unplanned downtime, cut costs, and improve profitability.

“With the help of Google Cloud Platform, we are helping our customers to be data-driven, which wasn’t possible before,” adds Willem. “They now understand that data is their most important asset. That allows them to be more innovative and continually improve their production processes.”

Picking the right database for your application is not easy. The choice depends heavily on your use case—transactional processing, analytical processing, in-memory database, and so on—but it also depends on other factors. This post covers the different database options available within Google Cloud across relational (SQL) and non-relational (NoSQL) databases and explains which use cases are best suited for each database option. 

Relational databases 

In relational databases information is stored in tables, rows and columns, which typically works best for structured data. As a result they are used for applications in which the structure of the data does not change often. SQL (Structured Query Language) is used when interacting with most relational databases. They offer ACID consistency mode for the data, which means:

• Atomic: All operations in a transaction succeed or the operation is rolled back.
• Consistent: On the completion of a transaction, the database is structurally sound.
• Isolated: Transactions do not contend with one another. Contentious access to data is moderated by the database so that transactions appear to run sequentially.
• Durable: The results of applying a transaction are permanent, even in the presence of failures.

Because of these properties, relational databases are used in applications that require high accuracy and for transactional queries such as financial and retail transactions. For example: In banking when a customer makes a funds transfer request, you want to make sure the transaction is possible and it actually happens on the most up-to-date account balance, in this case an error or resubmit request is likely fine.

There are three relational database options in Google Cloud: Cloud SQL, Cloud Spanner, and Bare Metal Solution.

Cloud SQL: Provides managed MySQL, PostgreSQL and SQL Server databases on Google Cloud. It reduces maintenance cost and automates database provisioning, storage capacity management, back ups, and out-of-the-box high availability and disaster recovery/failover. For these reasons it is best for general-purpose web frameworks, CRM, ERP, SaaS and e-commerce applications.

Cloud Spanner: Cloud Spanner is an enterprise-grade, globally-distributed, and strongly-consistent database that offers up to 99.999% availability, built specifically to combine the benefits of relational database structure with non-relational horizontal scale. It is a unique database that combines ACID transactions, SQL queries, and relational structure with the scalability that you typically associate with non-relational or NoSQL databases. As a result, Spanner is best used for applications such as gaming, payment solutions, global financial ledgers, retail banking and inventory management that require ability to scale limitlessly with strong-consistency and high-availability. 

Bare Metal Solution: Provides hardware to run specialized workloads with low latency on Google Cloud. This is specifically useful if there is an Oracle database that you want to lift and shift into Google Cloud. This enables data center retirements and paves a path to modernize legacy applications. 

Non-relational databases

Non-relational databases (or NoSQL databases) store compex, unstructured data in a non-tabular form such as documents. Non-relational databases are often used when large quantities of complex and diverse data need to be organized. Unlike relational databases, they perform faster because a query doesn’t have to access several tables to deliver an answer, making them ideal for storing data that may change frequently or for applications that handle many different kinds of data. 

For example, an apparel store might have a database in which shirts have their own document containing all of their information, including size, brand, and color with room for adding more parameters later such as sleeve size, collars, and so on.

Qualities that make NoSQL databases fast:

• Eventual consistency: stores usually exhibit consistency at some later point (e.g., lazily at read time)
• Horizontal scaling, usually using hashed distributions
• Typically, they are optimized for a specific workload pattern (i.e., key-value, graph, wide-column)
• Typically, they don’t support cross shard transactions or flexible isolation modes.

Because of these properties, non-relational databases are used in applications that require large scale, reliability, availability, and frequent data changes.They can easily scale horizontally by adding more servers, unlike some relational databases, which scale vertically by increasing the machine size as the data grows. Although, some relations databases such as Cloud Spanner support scale-out and strict consistency.

Non-relational databases can store a variety of unstructured data such as documents, key-value, graphs, wide columns, and more. Here are your non-relational database options in Google Cloud: 

• Document databases: Store information as documents (in formats such as JSON and XML). For example: Firestore
• Key-value stores: Group associated data in collections with records that are identified with unique keys for easy retrieval. Key-value stores have just enough structure to mirror the value of relational databases while still preserving the benefits of NoSQL. For example: Datastore, Bigtable, Memorystore
• In-memory database: Purpose-built database that relies primarily on memory for data storage. These are designed to attain minimal response time by eliminating the need to access disks. They are ideal for applications that require microsecond response times and can have large spikes in traffic. For example: Memorystore
• Wide-column databases: Use the tabular format but allow a wide variance in how data is named and formatted in each row, even in the same table. They have some basic structure while preserving a lot of flexibility. For example: Bigtable
• Graph databases: Use graph structures to define the relationships between stored data points; useful for identifying patterns in unstructured and semi-structured information. For example: JanusGraph

There are three non-relational databases in Google Cloud:

• Firestore: Is a serverless document database which scales on demand and acts as a backend-as-a-service. It is DBaaS that increases the speed of building applications. It is perfect for all general purpose uses cases such as ecommerce, gaming, IoT and real time dashboards. With Firestore users can interact with and collaborate on live and offline data making it great for real-time application and mobile apps.  
• Cloud Bigtable: Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, enabling you to store terabytes or even petabytes of data. It is ideal for storing very large amounts of single-keyed data with very low latency. It supports high read and write throughput at sub-millisecond latency, and it is an ideal data source for MapReduce operations. It also supports the open-source HBase API standard to easily integrate with the Apache ecosystem including HBase, Beam, Hadoop and Spark along with Google Cloud ecosystem.
• Memorystore: Memorystore is a fully managed in-memory data store service for Redis and Memcached at Google Cloud. It is best for in-memory and transient data stores and automates the complex tasks of provisioning, replication, failover, and patching so you can spend more time coding. Because it offers extremely low latency and high performance, Memorystore is great for web and mobile, gaming, leaderboard, social, chat, and news feed applications.

Conclusion

Choosing a relational or a non-relational database largely depends on the use case. Broadly, if your application requires ACID transactions and your data structure is not going to change much, select a relational database. 

In Google Cloud use Cloud SQL for any general-purpose SQL database and Cloud Spanner for large-scale globally scalable, strongly consistent use cases. In general, if your data structure may change later and if scale and availability is a bigger requirement than consistency then a non-relational database is a preferable choice.  Google Cloud offers Firestore, Memorystore, and Cloud Bigtable to support a variety of use cases across the document, key-value, and wide column database spectrum.

For more comparison resources on each database check out the overview. For more hands-on experience with Bigtable, check out our on-demand training here and learn about migrating databases to managed services check out this whitepaper.  

https://youtube.com/watch?v=2TZXSnCTd7E%3Fenablejsapi%3D1%26

For more #GCPSketchnote, follow the GitHub repo. For similar cloud content follow me on Twitter @pvergadia and keep an eye out on thecloudgirl.dev.