Category Archives: Tech Reviews

Amazon Q Developer review: Code completions, code chat, and AWS skills

Posted by Richy George on 24 June, 2024

This post was originally published on this site

When I reviewed Amazon CodeWhisperer, Google Bard, and GitHub Copilot in June of 2023, CodeWhisperer could generate code in an IDE and did security reviews, but it lacked a chat window and code explanations. The current version of CodeWhisperer is now called Amazon Q Developer, and it does have a chat window that can explain code, and several other features that may be relevant to you, especially if you do a lot of development using AWS.

Amazon Q Developer currently runs in Visual Studio Code, Visual Studio, JetBrains IDEs, the Amazon Console, and the macOS command line. Q Developer also offers asynchronous agents, programming language translations, and Java code transformations/upgrades. In addition to generating, completing, and discussing code, Q Developer can write unit tests, optimize code, scan for vulnerabilities, and suggest remediations. It supports conversations in English, and code in the Python, Java, JavaScript, TypeScript, C#, Go, Rust, PHP, Ruby, Kotlin, C, C++, shell scripting, SQL, and Scala programming languages.

You can chat with Amazon Q Developer about AWS capabilities, and ask it to review your resources, analyze your bill, or architect solutions. It knows about AWS well-architected patterns, documentation, and solution implementation.

According to Amazon, Amazon Q Developer is “powered by Amazon Bedrock” and trained on “high-quality AWS content.” Since Bedrock supports many foundation models, it’s not clear from the web statement which one was used for Amazon Q Developer. I asked, and got this answer from an AWS spokesperson: “Amazon Q uses multiple models to execute its tasks and uses logic to route tasks to the model that is the best fit for the job.”

Amazon Q Developer has a reference tracker that detects whether a code suggestion might be similar to publicly available code. The reference tracker can label these with a repository URL and project license information, or optionally filter them out.

Amazon Q Developer directly competes with GitHub Copilot, JetBrains AI, and Tabnine, and indirectly competes with a number of large language models (LLMs) and small language models (SLMs) that know about code, such as Code Llama, StarCoder, Bard, OpenAI Codex, and Mistral Codestral. GitHub Copilot can converse in dozens of natural languages, as opposed to Amazon Q Developer’s one, and supports a number of extensions from programming, cloud, and database vendors, as opposed to Amazon Q Developer’s AWS-only ties.

Installing Amazon Q Developer

Given the multiple environments in which Amazon Q Developer can run, it’s not a surprise that there are multiple installers. The only tricky bit is signing and authentication.

Installing Q Developer in Visual Studio Code

You can install Amazon Q Developer from the Visual Studio Code Marketplace, or from the Extensions sidebar in Visual Studio Code. You can get to that sidebar from the Extensions icon at the far left, by pressing Shift-Command-X, or by choosing Extensions: Install Extensions from the command palette. Type “Amazon Q” to find it. Once you’ve installed the extension, you’ll need to authenticate to AWS as discussed below.

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Installing Q Developer in JetBrains IDEs

Like Visual Studio Code, JetBrains has a marketplace for IDE plugins, where Amazon Q Developer is available. You’ll need to reboot the IDE after downloading and installing the plugin. Then you’ll need to authenticate to AWS as discussed below. Note that the Amazon Q Developer plugin disables local inline JetBrains full-line code completion.

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Installing Q Developer in the AWS Toolkit for Visual Studio

For Visual Studio, Amazon Q Developer is part of the AWS Toolkit, which you can find it in the Visual Studio Marketplace. Again, once you’ve installed the toolkit you’ll need to authenticate to AWS as discussed below.

Signing and authenticating Amazon Q Developer

The authentication process is confusing because there are several options and several steps that bounce between your IDE and web browser. You used to have to repeat this process frequently, but the product manager assures me that re-authentication should now only be necessary every three months.

Installing Q Developer for command line

Amazon Q Developer for the command line is currently for macOS only, although a Linux version is on the roadmap and documented as a remote target. The macOS installation is basically a download of a DMG file, followed by running the disk image, dragging the Q file to the applications directory, and running that Q app to install the CLI q program and a menu bar icon that can bring up settings and the web user guide. You’ll also need to authenticate to AWS, which will log you in.

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Amazon Q Developer in the AWS Console

If you are running as an IAM user rather than a root user, you’ll have to add IAM permissions to use Amazon Q Developer. Once you have permission, AWS should display an icon at the right of the screen that brings up the Amazon Q Developer interface.

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Evaluating Amazon Q Developer

According to AWS, “Amazon Q Developer Agent achieved the highest scores of 13.4% on the SWE-Bench Leaderboard and 20.5% on the SWE-Bench Leaderboard (Lite), a data set that benchmarks coding capabilities. Amazon Q security scanning capabilities outperform all publicly benchmarkable tools on detection across the most popular programming languages.”

Both of the quoted numbers are reflected on the SWE-Bench site, but there are two issues. Neither number has as yet been verified by SWE-Bench, and the Amazon Q Developer ranking on the Lite Leaderboard has dropped to #3. In addition, if there’s a supporting document on the web for Amazon’s security scanning claim, it has evaded my searches.

SWE-Bench, from Cornell, is “an evaluation framework consisting of 2,294 software engineering problems drawn from real GitHub issues and corresponding pull requests across 12 popular Python repositories.” The scores reflect the solution rates. The Lite data set is a subset of 300 GitHub issues.

Let’s explore how Amazon Q Developer behaves on the various tasks it supports in some of the 15 programming languages it supports. This is not a formal benchmark, but rather an attempt to get a feel for how well it works. Bear in mind that Amazon Q Developer is context sensitive and tries to use the persona that it thinks best fits the environment where you ask it for help.

Predictive inline code generation with Amazon Q Developer

I tried a softball question for predictive code generation and used one of Amazon’s inline suggestion examples. The Python prompt supplied was # Function to upload a file to an S3 bucket. Pressing Option-C as instructed got me the code below the prompt in the screenshot below, after an illegal character that I had to delete. I had to type import at the top to prompt Amazon Q to generate the imports for logging, boto3, and ClientError.

I also used Q Chat to tell me how to resolve the imports; it suggested a pip command, but on my system that fixed the wrong Python environment (v 3.11). I had to do a little sleuthing in the Frameworks directory tree to remind myself to use pip3 to target my current Python v 3.12 environment. I felt like singing “Daisy, Daisy” to Dave and complaining that my mind was going.

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I also tried Amazon’s two other built-in inline suggestion examples. The example to complete an array of fake users in Python mostly worked; I had to add the closing ] myself. The example to generate unit tests failed when I pressed Option-C: It generated illegal characters instead of function calls. (I’m starting to suspect an issue with Option-C in VS Code on macOS. It may or may not have anything to do with Amazon Q Developer.)

When I restarted VS Code, tried again, and this time pressed Return on the line below the comment, it worked fine, generating the test_sum function below.

# Write a test case for the above function.
def test_sum():
    """
    Unit test for the sum function.
    """
    assert sum(1, 2) == 3
    assert sum(-1, 2) == 1
    assert sum(0, 0) == 0

AWS shows examples of completion with Amazon Q Developer in up to half a dozen programming languages in its documentation. The examples, like the Python ones we’ve discussed, are either very simple, e.g. add two numbers, or relate to common AWS operations supported by APIs, such as uploading files to an S3 bucket.

Natural language to code generation with Amazon Q Developer

Since I now believed that Amazon Q Developer can generate Python, especially for its own test examples, I tried something a little different. As shown in the screenshot below, I created a file called quicksort.cpp, then typed an initial comment:

//function to sort a vector of generics in memory using the quicksort algorithm

Amazon Q Developer kept trying to autocomplete this comment, and in some cases the implementation as well, for different problems. Nevertheless it was easy to keep typing my specification while Amazon Q Developer erased what it had generated, and Amazon Q Developer eventually generated a nearly correct implementation.

Quicksort is a well-known algorithm. Both the C and C++ libraries have implementations of it, but they don’t use generics. Instead, you need to write type-specific comparison functions to pass to qsort. That’s historic, as the libraries were implemented before generics were added to the languages.

LlamaIndex review: Easy context-augmented LLM applications

Posted by Richy George on 17 June, 2024

This post was originally published on this site

“Turn your enterprise data into production-ready LLM applications,” blares the LlamaIndex home page in 60 point type. OK, then. The subhead for that is “LlamaIndex is the leading data framework for building LLM applications.” I’m not so sure that it’s the leading data framework, but I’d certainly agree that it’s a leading data framework for building with large language models, along with LangChain and Semantic Kernel, about which more later.

LlamaIndex currently offers two open source frameworks and a cloud. One framework is in Python; the other is in TypeScript. LlamaCloud (currently in private preview) offers storage, retrieval, links to data sources via LlamaHub, and a paid proprietary parsing service for complex documents, LlamaParse, which is also available as a stand-alone service.

LlamaIndex boasts strengths in loading data, storing and indexing your data, querying by orchestrating LLM workflows, and evaluating the performance of your LLM application. LlamaIndex integrates with over 40 vector stores, over 40 LLMs, and over 160 data sources. The LlamaIndex Python repository has over 30K stars.

Typical LlamaIndex applications perform Q&A, structured extraction, chat, or semantic search, and/or serve as agents. They may use retrieval-augmented generation (RAG) to ground LLMs with specific sources, often sources that weren’t included in the models’ original training.

LlamaIndex competes with LangChain, Semantic Kernel, and Haystack. Not all of these have exactly the same scope and capabilities, but as far as popularity goes, LangChain’s Python repository has over 80K stars, almost three times that of LlamaIndex (over 30K stars), while the much newer Semantic Kernel has over 18K stars, a little over half that of LlamaIndex, and Haystack’s repo has over 13K stars.

Repository age is relevant because stars accumulate over time; that’s also why I qualify the numbers with “over.” Stars on GitHub repos are loosely correlated with historical popularity.

LlamaIndex, LangChain, and Haystack all boast a number of major companies as users, some of whom use more than one of these frameworks. Semantic Kernel is from Microsoft, which doesn’t usually bother publicizing its users except for case studies.

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LlamaIndex features

At a high level, LlamaIndex is designed to help you build context-augmented LLM applications, which basically means that you combine your own data with a large language model. Examples of context-augmented LLM applications include question-answering chatbots, document understanding and extraction, and autonomous agents.

The tools that LlamaIndex provides perform data loading, data indexing and storage, querying your data with LLMs, and evaluating the performance of your LLM applications:

• Data connectors ingest your existing data from their native source and format.
• Data indexes, also called embeddings, structure your data in intermediate representations.
• Engines provide natural language access to your data. These include query engines for question answering, and chat engines for multi-message conversations about your data.
• Agents are LLM-powered knowledge workers augmented by software tools.
• Observability/Evaluation integrations enable you to experiment, evaluate, and monitor your app.

Context augmentation

LLMs have been trained on large bodies of text, but not necessarily text about your domain. There are three major ways to perform context augmentation and add information about your domain, supplying documents, doing RAG, and fine-tuning the model.

The simplest context augmentation method is to supply documents to the model along with your query, and for that you might not need LlamaIndex. Supplying documents works fine unless the total size of the documents is larger than the context window of the model you’re using, which was a common issue until recently. Now there are LLMs with million-token context windows, which allow you to avoid going on to the next steps for many tasks. If you plan to perform many queries against a million-token corpus, you’ll want to cache the documents, but that’s a subject for another time.

Retrieval-augmented generation combines context with LLMs at inference time, typically with a vector database. RAG procedures often use embedding to limit the length and improve the relevance of the retrieved context, which both gets around context window limits and increases the probability that the model will see the information it needs to answer your question.

Essentially, an embedding function takes a word or phrase and maps it to a vector of floating point numbers; these are typically stored in a database that supports a vector search index. The retrieval step then uses a semantic similarity search, often using the cosine of the angle between the query’s embedding and the stored vectors, to find “nearby” information to use in the augmented prompt.

Fine-tuning LLMs is a supervised learning process that involves adjusting the model’s parameters to a specific task. It’s done by training the model on a smaller, task-specific or domain-specific data set that’s labeled with examples relevant to the target task. Fine-tuning often takes hours or days using many server-level GPUs and requires hundreds or thousands of tagged exemplars.

Installing LlamaIndex

You can install the Python version of LlamaIndex three ways: from the source code in the GitHub repository, using the llama-index starter install, or using llama-index-core plus selected integrations. The starter installation would look like this:

pip install llama-index

This pulls in OpenAI LLMs and embeddings in addition to the LlamaIndex core. You’ll need to supply your OpenAI API key (see here) before you can run examples that use it. The LlamaIndex starter example is quite straightforward, essentially five lines of code after a couple of simple setup steps. There are many more examples in the repo, with documentation.

Doing the custom installation might look something like this:

pip install llama-index-core llama-index-readers-file llama-index-llms-ollama llama-index-embeddings-huggingface

That installs an interface to Ollama and Hugging Face embeddings. There’s a local starter example that goes with this installation. No matter which way you start, you can always add more interface modules with pip.

If you prefer to write your code in JavaScript or TypeScript, use LlamaIndex.TS (repo). One advantage of the TypeScript version is that you can run the examples online on StackBlitz without any local setup. You’ll still need to supply an OpenAI API key.

LlamaCloud and LlamaParse

LlamaCloud is a cloud service that allows you to upload, parse, and index documents and search them using LlamaIndex. It’s in a private alpha stage, and I was unable to get access to it. LlamaParse is a component of LlamaCloud that allows you to parse PDFs into structured data. It’s available via a REST API, a Python package, and a web UI. It is currently in a public beta. You can sign up to use LlamaParse for a small usage-based fee after the first 7K pages a week. The example given comparing LlamaParse and PyPDF for the Apple 10K filing is impressive, but I didn’t test this myself.

LlamaHub

LlamaHub gives you access to a large collection of integrations for LlamaIndex. These include agents, callbacks, data loaders, embeddings, and about 17 other categories. In general, the integrations are in the LlamaIndex repository, PyPI, and NPM, and can be loaded with pip install or npm install.

create-llama CLI

create-llama is a command-line tool that generates LlamaIndex applications. It’s a fast way to get started with LlamaIndex. The generated application has a Next.js powered front end and a choice of three back ends.

RAG CLI

RAG CLI is a command-line tool for chatting with an LLM about files you have saved locally on your computer. This is only one of many use cases for LlamaIndex, but it’s quite common.

LlamaIndex components

The LlamaIndex Component Guides give you specific help for the various parts of LlamaIndex. The first screenshot below shows the component guide menu. The second shows the component guide for prompts, scrolled to a section about customizing prompts.

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Learning LlamaIndex

Once you’ve read, understood, and run the starter example in your preferred programming language (Python or TypeScript, I suggest that you read, understand, and try as many of the other examples as look interesting. The screenshot below shows the result of generating a file called essay by running essay.ts and then asking questions about it using chatEngine.ts. This is an example of using RAG for Q&A.

The chatEngine.ts program uses the ContextChatEngine, Document, Settings, and VectorStoreIndex components of LlamaIndex. When I looked at the source code, I saw that it relied on the OpenAI gpt-3.5-turbo-16k model; that may change over time. The VectorStoreIndex module seemed to be using the open-source, Rust-based Qdrant vector database, if I was reading the documentation correctly.

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Bringing context to LLMs

As you’ve seen, LlamaIndex is fairly easy to use to create LLM applications. I was able to test it against OpenAI LLMs and a file data source for a RAG Q&A application with no issues. As a reminder, LlamaIndex integrates with over 40 vector stores, over 40 LLMs, and over 160 data sources; it works for several use cases, including Q&A, structured extraction, chat, semantic search, and agents.

I’d suggest evaluating LlamaIndex along with LangChain, Semantic Kernel, and Haystack. It’s likely that one or more of them will meet your needs. I can’t recommend one over the others in a general way, as different applications have different requirements.

Pros

1. Helps to create LLM applications for Q&A, structured extraction, chat, semantic search, and agents
2. Supports Python and TypeScript
3. Frameworks are free and open source
4. Lots of examples and integrations

Cons

1. Cloud is limited to private preview
2. Marketing is slightly overblown

Cost

Open source: free. LlamaParse import service: 7K pages per week free, then $3 per 1000 pages.

Platform

Python and TypeScript, plus cloud SaaS (currently in private preview).

DuckDB: The tiny but powerful analytics database

Posted by Richy George on 15 May, 2024

This post was originally published on this site

Most people assume that analytical databases, or OLAPs, are big, powerful beasts—and they are correct. Systems like Snowflake, Redshift, or Postgres involve a lot of setup and maintenance, even in their cloud-hosted incarnations. But what if all you want is “just enough” analytics for a dataset on your desktop? In that case, DuckDB is worth exploring.

Columnar data analytics on your laptop

DuckDB is a tiny but powerful analytics database engine—a single, self-contained executable, which can run standalone or as a loadable library inside a host process. There’s very little you need to set up or maintain with DuckDB. In this way, it is more like SQLite than the bigger analytical databases in its class.

DuckDB is designed for column-oriented data querying. It ingests data from sources like CSV, JSON, and Apache Parquet, and enables fast querying using familiar SQL syntax. DuckDB supports libraries for all the major programming languages, so you can work with it programmatically using the language of your choice. Or you can use DuckDB’s command-line interface, either on its own or as part of a shell pipeline.

Loading data into DuckDB

When you work with data in DuckDB, there are two modes you can use for that data. Persistent mode writes the data to disk so it can handle workloads bigger than system memory. This approach comes at the cost of some speed. In-memory mode keeps the data set entirely in memory, which is faster but retains nothing once the program ends. (SQLite can be used the same way.)

DuckDB can ingest data from a variety of formats. CSV, JSON, and Apache Parquet files are three of the most common. With CSV and JSON, DuckDB by default attempts to figure out the columns and data types on its own, but you can override that process as needed—for instance, to specify a format for a date column.

Other databases, like MySQL or Postgres, can also be used as data sources. You’ll need to load a DuckDB extension (more on this later) and provide a connection string to the database server; DuckDB doesn’t read the files for those databases directly. With SQLite, though, you connect to the SQLite database file as though it were just another data file.

To load data into DuckDB from an external source, you can use an SQL string, passed directly into DuckDB:

SELECT * FROM read_csv('data.csv');

You can also use methods in the DuckDB interface library for a given language. With the Python library for DuckDB, ingesting looks like this:

import duckdb
duckdb.read_csv("data.csv")

You can also query certain file formats directly, like Parquet:

SELECT * FROM 'test.parquet';

You can also issue file queries to create a persistent data view, which is usable as a table for multiple queries:

CREATE VIEW test_data AS SELECT * FROM read_parquet('test.parquet');

DuckDB has optimizations for working with Parquet files, so that it reads only what it needs from the file.

Other interfaces like ADBC and ODBC can also be used. ODBC serves as a connector for data visualization tools like Tableau.

Data imported into DuckDB can also be re-exported in many common formats: CSV, JSON, Parquet, Microsoft Excel, and others. This makes DuckDB useful as a data-conversion tool in a processing pipeline.

Querying data in DuckDB

Once you’ve loaded data into DuckDB, you can query it using SQL expressions. The format for such expressions is no different from regular SQL queries:

SELECT * FROM users WHERE ID>1000 ORDER BY Name DESC LIMIT 5;

If you’re using a client API to query DuckDB, you can pass SQL strings through the API, or you can use the client’s relational API to build up queries programmatically. In Python, reading from a JSON file and querying it might look like this:

import duckdb
file = duckdb.read_json("users.json")
file.select("*").filter("ID>1000").order("Name").limit(5)

If you use Python, you can use the PySpark API to query DuckDB directly, although DuckDB’s implementation of PySpark doesn’t yet support the full feature set.

DuckDB’s dialect of SQL closely follows most common SQL dialects, although it comes with a few gratuitous additions for the sake of analytics. For instance, placing the SAMPLE clause in a query lets you run a query using only a subset of the data in a table. The resulting query runs faster but it may be less accurate. DuckDB also supports the PIVOT keyword (for creating pivot tables), window functions and QUALIFY clauses to filter them, and many other analytics functions in its SQL dialect.

DuckDB extensions

DuckDB isn’t limited to the data formats and behaviors baked into it. Its extension API makes it possible to write third-party add-ons for DuckDB to support new data formats or other behaviors.

Some of the functionality included with DuckDB is implemented through first-party add-ons, like support for Parquet files. Others, like MySQL or Postgres connectivity, or vector similarity search, are also maintained by DuckDB’s team but provided separately.

DBOS: A better way to build applications?

Posted by Richy George on 29 April, 2024

This post was originally published on this site

At the end of March 2024, Mike Stonebraker announced in a blog post the release of DBOS Cloud, “a transactional serverless computing platform, made possible by a revolutionary new operating system, DBOS, that implements OS services on top of a distributed database.” That sounds odd, to put it mildly, but it makes more sense when you read the origin story:

The idea for DBOS (DataBase oriented Operating System) originated 3 years ago with my realization that the state an operating system must maintain (files, processes, threads, messages, etc.) has increased in size by about 6 orders of magnitude since I began using Unix on a PDP-11/40 in 1973. As such, storing OS state is a database problem. Also, Linux is legacy code at the present time and is having difficulty making forward progress. For example there is no multi-node version of Linux, requiring people to run an orchestrator such as Kubernetes. When I heard a talk by Matei Zaharia in which he said Databricks could not use traditional OS scheduling technology at the scale they were running and had turned to a DBMS solution instead, it was clear that it was time to move the DBMS into the kernel and build a new operating system.”

If you don’t know Stonebraker, he’s been a database-focused computer scientist (and professor) since the early 1970s, when he and his UC Berkeley colleagues Eugene Wong and Larry Rowe founded Ingres. Ingres later inspired Sybase, which was eventually the basis for Microsoft SQL Server. After selling Ingres to Computer Associates, Stonebraker and Rowe started researching Postgres, which later became PostgreSQL and also evolved into Illustra, which was purchased by Informix.

I heard Stonebraker talk about Postgres at a DBMS conference in 1980. What I got out of that talk, aside from an image of “jungle drums” calling for SQL, was the idea that you could add support for complex data types to the database by implementing new index types, extending the query language, and adding support for that to the query parser and optimizer. The example he used was geospatial information, and he explained one kind of index structure that would make 2D geometric database queries go very fast. (This facility eventually became PostGIS. The R-tree currently used by default in PostGIS GiST indexes wasn’t invented until 1984, so Mike was probably talking about the older quadtree index.)

Skipping ahead 44 years, it should surprise precisely nobody in the database field that DBOS uses a distributed version of PostgreSQL as its kernel database layer.

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DBOS features

DBOS Transact, an open-source TypeScript framework, supports Postgres-compatible transactions, reliable workflow orchestration, HTTP serving using GET and POST, communication with external services and third-party APIs, idempotent requests using UUID keys, authentication and authorization, Kafka integration with exactly-once semantics, unit testing, and self-hosting. DBOS Cloud, a transactional serverless platform for deploying DBOS Transact applications, supports serverless app deployment, time-travel debugging, cloud database management, and observability.

Let’s highlight some major areas of interest.

DBOS Transact

The code shown in the screenshot below demonstrates transactions, as well as HTTP serving using GET. It’s worthwhile to read the code closely. It’s only 18 lines, not counting blank lines.

The first import (line 1) brings in the DBOS SDK classes that we’ll need. The second import (line 2) brings in the Knex.js SQL query builder, which handles sending the parameterized query to the Postgres database and returning the resulting rows. The database table schema is defined in lines 4 through 8; the only columns are a name string and a greet_count integer.

There is only one method in the Hello class, helloTransaction. It is wrapped in @GetApi and @Transaction decorators, which respectively cause the method to be served in response to an HTTP GET request on the path /greeting/ followed by the username parameter you want to pass in and wrap the database call in a transaction, so that two instances can’t update the database simultaneously.

The database query string (line 16) uses PostgreSQL syntax to try to insert a row into the database for the supplied name and an initial count of 1. If the row already exists, then the ON CONFLICT trigger runs an update operation that increments the count in the database.

Line 17 uses Knex.js to send the SQL query to the DBOS system database and retrieves the result. Line 18 pulls the count out of the first row of results and returns the greeting string to the calling program.

The use of SQL and a database for what feels like should be a core in-memory system API, such as a Linux atomic counter or a Windows interlocked variable, seems deeply weird. Nevertheless, it works.

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DBOS Time Travel Debugger

When you run an application in DBOS Cloud it records every step and change it makes (the workflow) in the database. You can debug that using Visual Studio Code and the DBOS Time Travel Debugger extension. The time-travel debugger allows you to debug your DBOS application against the database as it existed at the time the selected workflow originally executed.

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DBOS Quickstart

The DBOS Quickstart tutorial requires Node.js 20 or later and a PostgreSQL database you can connect to, either locally, in a Docker container, or remotely. I already had Node.js v20.9.0 installed on my M1 MacBook, but I upgraded it to v20.12.1 from the Node.js website.

I didn’t have PostgreSQL installed, so I downloaded and ran the interactive installer for v16.2 from EnterpriseDB. This installer creates a full-blown macOS server and applications. If I had used Homebrew instead, it would have created command-line applications, and if I had used Postgres.app, I would have gotten a menu-bar app.

The Quickstart proper starts by creating a DBOS app directory using Node.js.

martinheller@Martins-M1-MBP ~ % npx -y @dbos-inc/create@latest -n myapp
Merged .gitignore files saved to myapp/.gitignore
added 590 packages, and audited 591 packages in 25s
found 0 vulnerabilities
added 1 package, and audited 592 packages in 1s
found 0 vulnerabilities
added 129 packages, and audited 721 packages in 5s
found 0 vulnerabilities
Application initialized successfully!

Then you configure the app to use your Postgres server and export your Postgres password into an enviroment variable.

martinheller@Martins-M1-MBP ~ % cd myapp
martinheller@Martins-M1-MBP myapp % npx dbos configure
? What is the hostname of your Postgres server? localhost
? What is the port of your Postgres server? 5432
? What is your Postgres username? postgres
martinheller@Martins-M1-MBP myapp % export PGPASSWORD=*********

After that, you create a “Hello” database using Node.js and Knex.js.

martinheller@Martins-M1-MBP myapp % npx dbos migrate
2024-04-09 15:01:42 [info]: Starting migration: creating database hello if it does not exist
2024-04-09 15:01:42 [info]: Database hello does not exist, creating...
2024-04-09 15:01:42 [info]: Executing migration command: npx knex migrate:latest
2024-04-09 15:01:43 [info]: Batch 1 run: 1 migrations
2024-04-09 15:01:43 [info]: Creating DBOS tables and system database.
2024-04-09 15:01:43 [info]: Migration successful!

With that complete, you build and run the DBOS app locally.

martinheller@Martins-M1-MBP myapp % npm run build
npx dbos start

> myapp@0.0.1 build
> tsc

2024-04-09 15:02:30 [info]: Workflow executor initialized
2024-04-09 15:02:30 [info]: HTTP endpoints supported:
2024-04-09 15:02:30 [info]:     GET   :  /greeting/:user
2024-04-09 15:02:30 [info]: DBOS Server is running at http://localhost:3000
2024-04-09 15:02:30 [info]: DBOS Admin Server is running at http://localhost:3001
^C

At this point, you can browse to http://localhost:3000 to test the application. That done, you register for the DBOS Cloud and provision your own database there.

martinheller@Martins-M1-MBP myapp % npx dbos-cloud register -u meheller
2024-04-09 15:11:35 [info]: Welcome to DBOS Cloud!
2024-04-09 15:11:35 [info]: Before creating an account, please tell us a bit about yourself!
Enter First/Given Name: Martin
Enter Last/Family Name: Heller
Enter Company: self
2024-04-09 15:12:06 [info]: Please authenticate with DBOS Cloud!
Login URL: https://login.dbos.dev/activate?user_code=QWKW-TXTB
2024-04-09 15:12:12 [info]: Waiting for login...
2024-04-09 15:12:17 [info]: Waiting for login...
2024-04-09 15:12:22 [info]: Waiting for login...
2024-04-09 15:12:27 [info]: Waiting for login...
2024-04-09 15:12:32 [info]: Waiting for login...
2024-04-09 15:12:38 [info]: Waiting for login...
2024-04-09 15:12:44 [info]: meheller successfully registered!
martinheller@Martins-M1-MBP myapp % npx dbos-cloud db provision iw_db -U meheller
Database Password: ********
2024-04-09 15:19:22 [info]: Successfully started provisioning database: iw_db
2024-04-09 15:19:28 [info]: {"PostgresInstanceName":"iw_db","HostName":"userdb-51fcc211-6ed3-4450-a90e-0f864fc1066c.cvc4gmaa6qm9.us-east-1.rds.amazonaws.com","Status":"available","Port":5432,"DatabaseUsername":"meheller","AdminUsername":"meheller"}
2024-04-09 15:19:28 [info]: Database successfully provisioned!

Finally, you can register and deploy your app in the DBOS Cloud.

martinheller@Martins-M1-MBP myapp % npx dbos-cloud app register -d iw_db
2024-04-09 15:20:09 [info]: Loaded application name from package.json: myapp
2024-04-09 15:20:09 [info]: Registering application: myapp
2024-04-09 15:20:11 [info]: myapp ID: d8806829-c5b8-4df0-8b5a-2d1bf87c3322
2024-04-09 15:20:11 [info]: Successfully registered myapp!
martinheller@Martins-M1-MBP myapp % npx dbos-cloud app deploy
2024-04-09 15:20:35 [info]: Loaded application name from package.json: myapp
2024-04-09 15:20:35 [info]: Submitting deploy request for myapp
2024-04-09 15:21:09 [info]: Submitted deploy request for myapp. Assigned version: 1712676035
2024-04-09 15:21:13 [info]: Waiting for myapp with version 1712676035 to be available
2024-04-09 15:21:21 [info]: Successfully deployed myapp!
2024-04-09 15:21:21 [info]: Access your application at https://meheller-myapp.cloud.dbos.dev/

IDG

DBOS applications

The “Hello” application does illustrate some of the core features of DBOS Transact and the DBOS Cloud, but it’s so basic that it’s barely a toy. The Programming Quickstart adds a few more details, and it’s worth your time to go through it. You’ll learn how to use communicator functions to access third-party services (email, in this example) as well as how to compose reliable workflows. You’ll literally interrupt the workflow and restart it without re-sending the email: DBOS workflows always run to completion and each of their operations executes once and only once. That’s possible because DBOS persists the output of each step in your database.

Once you’ve understood the programming Quickstart, you’ll be ready to try out the two DBOS demo applications, which do rise to the level of being toys. Both demos use Next.js for their front ends, and both use DBOS workflows, transactions, and communicators.

The first demo, E-Commerce, is a web shopping and payment processing system. It’s worthwhile reading the Under the Covers section of the README in the demo’s repository to understand how it works and how you might want to upgrade it to, for example, use a real-world payment provider.

The second demo, YKY Social, simulates a simple social network, and uses TypeORM rather than Knex.js for its database code. It also uses Amazon S3 for profile photos. If you’re serious about using DBOS yourself, you should work though both demo applications.

A tantalizing glimpse

I have to say that DBOS and DBOS Cloud look very interesting. Reliable execution and time-travel debugging, for example, are quite desirable. On the other hand, I wouldn’t want to build a real application on DBOS or DBOS Cloud at this point. I have lots of questions, starting with “How does it scale in practice?” and probably ending with “How much will it cost at X scale?”

I mentioned earlier that DBOS code looks weird but works. I would imagine that any programming shop considering writing an application on it would be discouraged or even repelled by the “it looks weird” part, as developers tend to be set in their ways until what they are doing no longer works.

I also have to point out that the current implementation of DBOS is very far from the system diagram you saw near the beginning of this review. Where’s the minimal kernel? DBOS currently runs on macOS, Linux, and Windows. None of those are minimal kernels. DBOS Cloud currently runs on AWS. Again, not a minimal kernel.

So, overall, DBOS is a tantalizing glimpse of something that may eventually turn out to be cool. It’s new and shiny, and it comes from smart people, but it will be awhile before it could possibly become a mainstream system.

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Cost: Free with usage limits; paid plans require you to contact sales.

Platform: macOS, Linux, Windows, AWS.