The Azure Static Web App feature is relatively new in the Azure estate which has recently become generally available, I thought I would take it for a test drive and discuss my findings.
I am a proponent of the JAMStack architecture for front end applications and a user of CD enabled CDN services like Netlify, so this Azure feature was naturally appealing to me.
The major advantage to using is simplicity, no scaffolding or infra requirements and it is seamlessly integrated into your CI/CD processes (natively if you are using GitHub).
Deploying Static Web Apps in Azure
Pretty simple to setup, aside from a name and a resource group, you just need to supply:
a location (Azure region to be used for serverless back end APIs via Azure Function Apps) note that this is not a location where the static web is necessarily running
a GitHub or GitLab repo URL
the branch you wish to use to trigger production deployments (e.g. main)
a path to your code within your app (e.g. where your package.json file is located)
an output folder (e.g. dist) this should not exist in your repo
a project or personal access token for your GitHub account (alternatively you can perform an interactive OAuth2.0 consent if using the portal)
An example is shown here:
Using the consent provided (either using the OAuth flow or by providing a token), Azure Static Web Apps will automagically create the GitHub Actions workflow to deploy your application on a push or merge event to your repo. This includes providing scoped API credentials to Azure to allow access to the Static Web App resource using secrets in GitHub (which are created automagically as well). An example workflow is shown here:
Preview or Staging Releases
Similar to the functionality in analogous services like Netlify, you can configure preview releases of your application to be deployed from specified branches on pull request events.
Routes and Authorization
Routes (for SPAs) need to be provided to Azure by using a file named staticwebapp.config.json located in the application root of your repo (same level as you package.json file). You can also specify response codes and whether the rout requires authentication as shown here:
Globally distributed CDN
Increased security posture, reduced attack surface area
Big fan of GitLab (and GitLab CI in particular). I had a recent requirement to push changes to a wiki repo associated with a GitLab project through a GitLab CI pipeline (using the SaaS version of GitLab) and ran into a conundrum…
Using the GitLab SaaS version – deploy tokens can’t have write api access, so the next best solution is to use deploy keys, adding your public key as a deploy key and granting this key write access to repositories is relatively straightforward.
This issue is when you attempt to create a masked GitLab CI variable using the private key from your keypair, you get this…
I was a bit astonished to see this to be honest… Looks like it has been raised as an issue several times over the last few years but never resolved (the root cause of which is something to do with newline characters or base64 encoding or the overall length of the string).
I came up with a solution! Not pretty but effective, masks the variable so that it cannot be printed in CI logs as shown here:
Add a masked and protected GitLab variable for each line in the private key, for example:
Add the following block to your .gitlab-ci.yml file:
now within Jobs in your pipeline you can simply do this to clone, push or pull from a remote GitLab repo:
as mentioned not pretty, but effective and no other cleaner options as I could see…
Identity and Access Management is a critical component of any application or SaaS architecture. I’m currently doing a spike of the Okta solution for an application development project I am on. Okta is a comprehensive solution built on the open OAuth2 and OIDC protocols, as well as supporting more conventional identity federation approaches such as SAML.
Okta has a clean and easy to use web-based Admin interface which can be used to create applications, users, claims, identity providers and more.
During my spike, which was done in a crash and burn test Okta organisation, I had associated my user account with a Microsoft Identity Provider for SSO, and subsequently had issues accessing the Microsoft Account my user was associated with, as a result I managed to lock myself (the super admin) out of the Okta Admin Console.
Being in lockdown and having some free time on my hands, I decided to create a simple open source command line tool which could be used to administer an Okta organisation. The result of this weekend lockdown is okta-admin…
For this project I used the Golang SDK for Okta, along with the Cobra and Viper Golang packages (used by docker, kubectl and other popular command line utilities). To provide a query interface to JSON response payloads I use GJson.
Snowflake allows roles to be assigned to other roles, so when a user is assigned to a role, they may inherit the ability to use countless other roles.
Challenge: recursively enumerate all roles for a given user
One solution would be to create a complex query on the “SNOWFLAKE"."ACCOUNT_USAGE"."GRANTS_TO_ROLES" object.
An easier solution is to use a stored procedure to recurse through grants for a given user and return an ARRAY of roles for that user.
To call the stored proc, execute:
One drawback of stored procedures in Snowflake is that they can only have scalar or array return types and cannot be used directly in a SQL query, however you can use the table(result_scan(last_query_id())) trick to get around this, as shown below where we will pivot the ARRAY into a record set with the array elements as rows:
This query must be the next statement run immediately after the CALL statement and cannot be run again until you run another CALL statement.
One of the foundational concepts in microservices architecture and design patterns is the concept of Orchestration versus Choreography. Before we look at a reference implementation of each of these patterns, it is worthwhile starting with an analogy.
This is often likened to a Jazz band versus a Symphony Orchestra.
A modern symphony orchestra is normally comprised of sections such as strings, brass, woodwind and percussion sections. The sections are orchestrated by a conductor, usually placed at a central point with respect to each of the sections. The conductor instructs each section to perform their components of the overall symphony.
By contrast, a Jazz band does not have a conductor and also features improvisation, with different musicians improvising based upon each other. Choreography, although more aligned to dance, can involve improvisation. In both cases there is still an intended output and a general framework as to how the composition will be executed, however unlike a symphony orchestra there is a degree of spontaneity.
Now back to technology and microservices…
In the Orchestration model, there is a central orchestration service which controls the interactions between other services, in other words the flow and control of communication and/or message passing between services is controlled by an orchestrator (much like the conductor in a symphony orchestra). On the plus side, this model enables easier monitoring and policy enforcement across the system. A generalisation of the Orchestration model is shown below:
By contrast, in the Choreography model, each service works independently and interacts with other services through events. In this model each service registers and emits events as they need to. The flow (of communication between services) is not predefined, much like a Jazz band. This model often includes a central broker for message passing between services, but the services operate independently of each other and are not controlled by a central service (an orchestrator). A generalisation of the Choreography model is shown below:
We will post subsequent articles with implementations of these patterns, but it is worthwhile getting a foundational understanding first.
Function Apps, Logic Apps and App Services can be used to expose APIs within Azure API Management which is an easy way to deploy serverless microservices. You can see this capability in the Azure portal below within API Management:
Like most readers, I like to script everything, so I was initially frustrated when I couldn’t replicate this operation in the Azure cli, REST, PowerShell, or any of the other SDKs or IaC tools. Others shared my frustration as seen here.
I was nearly resigned to using click ops in the portal (arrrgh) before I worked out this workaround.
There is a bit more prep work required to automate this process, but it is well worth it.
1. Create an OpenApi (or Swagger spec or WADL) specification document, as seen below (use the absolute URL for your Function App in the url parameter):
2. Use the az apim api import function (not the az apim api create function), as shown here:
3. Associate the API with a product (which is how you can rate limit APIs)
That’s it! You can now access your function via the API gateway using the gateway url or via the developer portal as seen below:
Following on from the recent post GCP Templates for C4 Diagrams using PlantUML, cloud architects are often challenged with producing diagrams for architectures spanning multiple cloud providers, particularly as you elevate to enterprise level diagrams.
In this post, with the magic of !includeurl we have brought PlantUML template libraries together for AWS, Azure and GCP icon sets, allowing us to produce multi cloud C4 diagrams using PlantUML like this one:
Creating a multi cloud diagram is simple, start by adding the following include statements after the @startuml label in a new PlantUML C4 diagram:
Then add references to the required services from different providers…
Then include the predefined resources from your different cloud providers in your diagram as shown here (describing a client server application over a cloud to cloud VPN between Azure and GCP)…
This is a follow up to the original Cloud Bigtable primer where we discussed the basics of Cloud Bigtable:
In this article we will cover schema design and row key selection in Bigtable – arguably the most critical design decision to make when employing Bigtable in a cloud data architecture.
Recall from the previous post where the Bigtable data model was introduced that tables in Bigtable are comprised of rows and columns – much like a table in any other RDBMS. Every row is uniquely identified by a rowkey – again akin to a primary key in a table in an RDBMS. But this is where the similarities end.
Unlike a table in an RDBMS, columns only ever exist when they are inserted, and NULLs are not stored. See the illustration below:
Row Key Selection
Data in Bigtable is distributed by row keys. Row keys are physically stored in tablets in lexographic order. Recall that row keys are your ONLY indexes to data in Bigtable.
As row keys are your only indexes to retrieve or update rows in Bigtable, row key design must take the access patterns for the data to be stored and served via Bigtable into consideration, specifically the following must be considered when designing a Bigtable application:
Search patterns (returning data for a specific entity)
Scan patterns (returning batches of data)
Queries that use the row key, a row prefix, or a row range are the most efficient. Queries that do not include a row key will typically scan GB or TB of data and would not be suitable for operational use cases.
Row Key Performance
Row key performance will be biased towards your specific access patterns and application functional requirements. For example if you are performing sequential reads or scan operations then sequential keys will perform the best, however their write performance will not be optimal. Conversely, random keys (such as a uuid) will perform best for writes but poor for scan or sequential read operations.
Adding salts to keys (or additional data), similar to the use of salts in cryptography as well as promoting other field keys to be part of a composite row key can help achieve a “Goldilocks” scenario for both reads and writes, see the diagram below:
Using Reverse Timestamps
Use reverse timestamps when your most common query is for the latest values. Typically you would append the reverse timestamp to the key, this will ensure that the same related records are grouped together, for instance if you are storing events for a customer using the customer id along with an appended reverse timestamp (for example <customer_id>#<reverse_ts>) would allow you to quickly serve the latest events for a customer in descending order as within each group (customer_id), rows will be sorted so most recent insert will be located at the top. A reverse timestamp can be generalised as:
Long.MAX_VALUE - System.currentTimeMillis()
Schema Design Tips
Some general tips for good schema design using Bigtable are summarised below:
Group related data for more efficient reads using column families
Distribute data evenly for more efficient writes
Place identical values in the adjoining rows for more efficient compression using row keys
Following these tips will give you the best possible performance using Bigtable.
Use the Key Visualizer to profile performance
Google provides a neat tool to visualize your row key distribution in Cloud Bigtable. You need to have at least 30 GB of data in your table to enable this feature.
The Key Visualizer is shown here:
The Key Visualizer will help you find and prevent hotspots, find rows with too much data and show if your key schema is balanced.
Bigtable is one of the original and best (massively) distributed NoSQL platforms available. Schema and moreover row key design play a massive part in ensuring low latency and query performance. Go forth and conquer with Cloud Bigtable!
I am a believer in the mantra of “Everything-as-Code”, this includes diagrams and other architectural artefacts. Enter PlantUML…
PlantUML is an open-source tool which allows users to create UML diagrams from an intuitive DSL (Domain Specific Language). PlantUML is built on top of Graphviz and enables software architects and designers to use code to create Sequence Diagrams, Use Case Diagrams, Class Diagrams, State and Activity Diagrams and much more.
PlantUML can be extended to support the C4 model for visualising software architecture. Which describes software in different layers including Context, Container, Component and Code diagrams.
GCP Architecture Diagramming using C4
PlantUML and C4 can be used to produce cloud architectures, there are official libraries available through PlantUML for Azure and AWS service icons, however these do not exist for GCP yet. There are several open source libraries available, however I have made an attempt to simplify the implementation.
The code below can be used to generate a C4 diagram describing a GCP architecture including official GCP service icons:
Bigtable is one of the foundational services in the Google Cloud Platform and to this day one of the greatest contributions to the big data ecosystem at large. It is also one of the least known services available, with all the headlines and attention going to more widely used services such as BigQuery.
In 2006 (pre Google Cloud Platform), Google released a white paper called “Bigtable: A Distributed Storage System for Structured Data”, this paper set out the reference architecture for what was to become Cloud Bigtable. This followed several other whitepapers including the GoogleFS and MapReduce whitepapers released in 2003 and 2004 which provided abstract reference architectures for the Google File System (now known as Colossus) and the MapReduce algorithm. These whitepapers inspired a generation of open source distributed processing systems including Hadoop. Google has long had a pattern of publicising a generalized overview of their approach to solving different storage and processing challenges at scale through white papers.
The Bigtable white paper inspired a wave of open source distributed key/value oriented NoSQL data stores including Apache HBase and Apache Cassandra.
What is Bigtable?
Bigtable is a distributed, petabyte scale NoSQL database. More specifically, Bigtable is…
At its core Bigtable is a distributed map or an associative array indexed by a row key, with values in columns which are created only when they are referenced. Each value is an uninterpreted byte array.
Row keys are stored in lexographic order akin to a clustered index in a relational database.
A given row can have any number of columns, not all columns must have values and NULLs are not stored. There may also be gaps between keys.
All values are versioned with a timestamp (or configurable integer). Data is not updated in place, it is instead superseded with another version.
When (and when not) to use Bigtable
You need to do many thousands of operations per second on TB+ scale data
Your access patterns are well known and simple
You need to support random write or random read operations (or sequential reads) – each using a row key as the primary identifier
Don’t use Bigtable if…
You need explicit JOIN capability, that is joining one or more tables
You need to do ad-hoc analytics
Your access patterns are unknown or not well defined
Bigtable vs Relational Database Systems
The following table compares and contrasts Bigtable against relational databases (both transaction oriented and analytic oriented databases):
Column Family Oriented
Single Row Only
Depends (but usually no)
Row Key Only
Yes (typically PI based)
Max Data Size
'00s GB to TB
Bigtable Data Model
Tables in Bigtable are comprised of rows and columns (sounds familiar so far..). Every row is uniquely identified by a rowkey (like a primary key..again sounds familiar so far).
Columns belong to Column Families and only exist when inserted, NULLs are not stored – this is where it starts to differ from a traditional RDBMS. The following image demonstrates the data model for a fictitious table in Bigtable.
In the previous example, we created two Column Families (cf1 and cf2). These are created during table definition or update operations (akin to DDL operations in the relational world). In this case, we have chosen to store primary attributes, like name, etc in cf1 and features (or derived attributes) in cf2 like indicators.
Each cell has a timestamp/version associated with it, multiple versions of a row can exist. Versions are naturally stored in descending order.
Properties such as the max age for a cell or the maximum number of versions to be stored for any given cell are set on the Column Family. Versions are compacted through a process called Garbage Collection – not to be confused with Java Garbage Collection (albeit same idea).
Bigtable Instances, Clusters, Nodes and Tables
Bigtable is a “no-ops” service, meaning you do not need to configure machine types or details about the underlying infrastructure, save a few sizing or performance options – such as the number of nodes in a cluster or whether to use solid state hard drives (SSD) or the magnetic alternative (HDD). The following diagram shows the relationships and cardinality for Cloud Bigtable.
Clusters and nodes are the physical compute layer for Bigtable, these are zonal assets, zonal and regional availability can be achieved through replication which we will discuss later in this article.
Instances are a virtual abstraction for clusters, Tables belong to instances (not clusters). This is due to Bigtables underlying architecture which is based upon a separation of storage and compute as shown below.
Bigtables separation of storage and compute allow it to scale horizontally, as nodes are stateless they can be increased to increase query performance. The underlying storage system in inherently scalable.
Physical Storage & Column Families
Data (Columns) for Bigtable is stored in Tablets (as shown in the previous diagram), which store “regions” of row keys for a particular Column Family. Columns consist of a column family prefix and qualifier, for instance:
A table can have one or more Column Families. Column families must be declared at schema definition time (could be a create or alter operation). A cell is an intersection of a row key and a version of a column within a column family.
Storage settings (such as the compaction/garbage collection properties mentioned before) can be specified for each Column Family – which can differ from other column families in the same table.
Bigtable Availability and Replication
Replication is used to increase availability and durability for Cloud Bigtable – this can also be used to segregate read and write operations for the same table.
Data and changes to tables are replicated across multiple regions or multiple zones within the same region, this replication can be blocking (single row transactions) or non blocking (eventually consistent). However all clusters within a Bigtable instance are considered primary (writable).
Requests are routed using Application Profiles, a single-cluster routing policy can be used for manual failover, whereas a multi-cluster routing is used for automatic failover.
Backup and Export Options for Bigtable
Managed backups can be taken at a table level, new tables can be created from backups. The backups cannot be exported, however table level export and import operations are available via pre-baked Dataflow templates for data stored in GCS in the following formats:
Bigtable data and admin functions are available via:
cbt (optional component of the Google SDK)
hbase shell (REPL shell)
Happybase API (Python API for Hbase)
SDK libraries for:
C#, C++, PHP, and more
As Bigtable is not a cheap service, there is a local emulator available which is great for application development. This is part of the Cloud SDK, and can be started using the following command:
gcloud beta emulators bigtable start
In the next article in this series we will demonstrate admin and data functions as well as the local emulator.