Improving the performance of data storage for a content-driven web application
is an iterative process. It is important to consider the potential throughput
and data volume that the application needs to support, in conjunction with its
database structure and data storage technologies. These are important
considerations that should also be made during the initial design phase.
Techniques such as multi-dimensional scaling and caching can help your
application grow further to support more users and data.
Throughput of processing data
The throughput of processing data refers to the rate at which data is processed
between devices, systems, or components within a computing environment. It
measures the capacity of a system to process data within a specific period.
Throughput is expressed in units of data per unit of time, such as bytes per
second (B/s), megabits per second (Mbps), or transactions per second (TPS).
The throughput of processing data refers to the data the database can process in
a given time. This is important for content-driven web applications, as they
often need to process large amounts of data in real-time.
Factors that can affect the throughput of processing data include:
- The type of database. Some databases are more efficient at processing data
than others.
- The hardware configuration. The database server should have enough CPU,
memory, and storage to handle the load.
- The query complexity. Complex queries can take longer to process than basic
queries.
Relating to scaling, throughput is a critical metric that measures the capacity
and efficiency of your content-driven web application to handle requests and
process data. High throughput is essential for managing increased traffic and
maintaining responsive performance.
Considerations relating to throughput when scaling a web application include:
Load Balancing
A load balancer evenly distributes traffic among your application's server instances. This
ensures that no single server becomes a bottleneck, and you can take advantage of the combined
processing power of multiple servers.
Horizontal Scaling
Horizontal scaling can be used to increase throughput. This involves adding more server
instances to your application's infrastructure. Horizontal scaling allows your application to
distribute requests across multiple servers, increasing throughput.
Database Scaling
If applicable, consider database scaling techniques to handle increased read and write
operations. These techniques can include using a distributed database or optimizing database
queries and indexing.
Database Autoscaling
Autoscaling mechanisms can add or remove server instances based on real-time traffic
patterns. Cloud platforms often provide autoscaling features to adjust resources to match
demand. Consider what scaling features are available for your database and platform, for
example a service such as Autoscaler for Spanner
.
Code/Algorithm Optimization
Consistently review and optimize your application's code and algorithms for efficiency. Minimizing server
resource consumption may lead to higher throughput.
Regional/Global
Regional and global data storage refers to the different ways in which data is stored and
managed across different geographical locations. Regional data storage involves storing data
locally in a specific region or location, while global data storage involves storing data on
servers located in different parts of the world. The choice between regional and global data
storage depends on factors such as the size of the data, the level of security required, and
the accessibility of the data to different users or applications.
Distribution
Data storage distribution involves distributing data across multiple physical or virtual
locations to ensure availability, performance, and security. Methods include distributed
storage systems and cloud solutions, chosen based on factors such as data size and complexity,
organizational needs, and available technology. A well-planned strategy can provide
redundancy, scalability, and other benefits.
Achieving high throughput involves optimizing a range of aspects of a system,
from hardware and software to network infrastructure and data processing
algorithms. The specific techniques and optimizations necessary for your
content-driven web application depend on your individual needs relating to data
processing tasks and potential constraints of the system.
Data volume
Scaling a web application in terms of the quantity of data stored involves
managing and expanding your data storage capacity to accommodate growing data
volumes. When scaling, you need to plan for a storage infrastructure that can
handle any data growth challenges.
Considerations for data storage when scaling a web application include:
Types of data stored
Some data types, such as images, videos, or binary data, may be more challenging to store or
take up more space than other data types. Complex data types or structures may also complicate
how a system can scale. Consider the type of data your application stores and whether it is
the optimal choice. Alternatively, consider storage systems built specifically for your use
case or data, such as blob storage, media storage systems, or time series databases.
Number of users
Consider the future growth of your application. As you onboard more users, more data must be
stored, accessed, and processed in your system. Scaling reads and writes
based on your application
performance profile may help optimize your application further as demands increase.
Data Storage Technology Choice
Evaluate your realistic data storage needs and choose the most appropriate data storage
technology, such as NoSQL and SQL. When making your choice, consider data structure, access
patterns, and scalability requirements.
Data Partitioning
Implementing data partitioning or sharding strategies to distribute data across multiple
storage nodes may allow you to distribute the data load and, therefore, scale more
effectively. You can partition by user, geographic location, or follow other criteria based on
your application's data access patterns.
Caching and Data Compression
Using caching strategies to store frequently accessed data in memory and to reduce the load on
your storage systems. Data compression techniques may also minimize storage requirements and
improve data transfer efficiency.
Data Lifecycle Management
Implement data lifecycle policies to manage data retention, archival, and deletion.
Database Indexing
If applicable, optimize database indexing, query performance, and database schema design to
handle growing datasets efficiently.
Data storage when scaling your web application is an ongoing process and must
align to the needs of your web application. Be sure to regularly assess storage
requirements, monitor performance, and adjust your storage infrastructure and
strategies as needed to ensure that it can handle increasing data quantities
effectively and efficiently.
Cache frequently accessed data
Caching data is a critical component of your scaling strategy; it helps improve
response times, reduces load on backend systems, and enhances the overall user
experience. Caching refers to storing frequently accessed data in a temporary
storage layer so that it can be retrieved quickly without repeatedly generating
the data from the source.
Consider the type of data your application frequently accesses, the pattern and
frequency with which data is retrieved, and its size.
Example caching strategies when scaling a web application include:
Object Caching
Object caching refers to caching individual objects such as database query results, API
responses, or HTML fragments. Popular options include Redis, Memcached, and Varnish.
Page Caching
Page caching involves caching entire web pages. This option is suited for static content and
can be implemented using reverse proxies, static site generators, or CDNs.
Database and Query Caching
Database and query caching refers to caching the results of frequently executed database
queries to reduce the load on your database server.
Full-Page Caching
Full-page caching can be used for dynamic content. It refers to caching entire rendered HTML
pages to minimize load on the server and database.
Client-Side Caching
Client-side caching refers to mechanisms such as browser caching to store assets locally on
users' devices. You can specify cache-control headers to control the time for which the assets
are cached in browsers.
Caching is an important component of a broader performance optimization strategy
to ensure that your application can handle increased user traffic and data
demands effectively and efficiently.
Scale reads and writes
Consider the throughput and data volume requirements together with potential
caching optimizations to identify how the data storage of your application can
scale further. When selecting a data storage system, it is important to consider
the impact of reads and writes. One way to think about this is by considering
the type of operations your application supports - and optimizing your data
storage for these use cases and usage patterns. For example, some operations may
benefit from asymmetric or symmetric scaling.
Asymmetric Scaling
In some cases, content-driven web applications may experience read or write
asymmetry. This means that there may be a significantly higher volume of reads
or writes than the other type of operation. In these cases, it is important to
scale the database accordingly. Consider your application and how data is
accessed and written.
Factors that can affect the read or writes asymmetry include:
- The type of application. Some applications are more read-intensive than
others.
- The user behavior. The way that users interact with the application can also
affect the read or write asymmetry.
Symmetric Scaling
Symmetric scaling means that both read and write operations can be scaled
linearly without bottlenecks as the application grows. Symmetric scaling for
read and write operations in a content-driven web application can be challenging
to achieve, especially when managing large volumes of data and high user
traffic.
Strategies for achieving symmetric scaling include:
Distributed Databases
Databases like Google Cloud Bigtable
are built
to distribute data across multiple nodes and provide scalability for read and write
operations.
Data Partitioning
Data partitioning ensures that data is evenly distributed and that each partition or shard can
handle both read and write operations independently.
Caching Layers
Implement caching layers for ready-driven workloads. Cache frequently accessed data to reduce
the load on the database for read operations.
Asynchronous Processing
You can offload time-consuming or non-urgent write operations to background processes or
queues using asynchronous processing. Your application continues to process reads without
waiting for writes to complete.
Symmetric scaling requires a comprehensive approach to the entire application
stack, from frontend to backend and from the database to the caching layers.
Symmetric scaling contributes to performance consistency, balanced resource
utilization, high availability, and scalability across dimensions. Be sure to
adjust your scaling strategy as needed and be prepared to address bottlenecks as
they arise.
