MetricFlow strives to make what has historically been an extremely repetitive process, writing SQL queries on core normalized data models, much more DRY. MetricFlow consolidates the definitions for joins, aggregations, filters, etc., and programmatically generates SQL to construct data marts. You can think of it like LookML, but more powerful and ergonomic (and open source!). The project has three components:
1. MetricFlow Spec: The specification encapsulates metric logic in a more reusable set of abstractions: data_sources, measures, dimensions, identifiers, metrics, and materializations.
2. DataFlow Planner: The Query Planner is a generalized SQL constructor. We take in data sources (ideally normalized data models) and generate a graph of data transformations (a flow, if you will) – joins, aggregations, filters, etc. We take that graph and render it down to db-specific SQL while optimizing it for performance and legibility.
3. MetricFlow Interfaces: The CLI and Python SDK rely on the flexibility of the Spec and Planner to build just about any query you could ask for on top of your data warehouse.
These components enable novel features that other semantic layers struggle to support today:
- MetricFlow enables the user to traverse the entire graph of a company’s data warehouse without confining their analysis to pre-built data models (dbt), Explores (in Looker), or Cubes (in lots of tools).
- The Metric abstraction allows the construction of complex metrics that traverse the graph described above to rely on multiple data sources. We support several common metric types today, and adding more is a critical part of the open-source roadmap.
- The Materialization abstraction allows users to define and then programmatically generate data marts that rely on a single DRY expression of the metrics and dimensions.
MetricFlow is open source(https://github.com/transform-data/metricflow) and distributed through pypi (`pip install metricflow`). You can set up (`mf setup`) a set of sample configs and try out a tutorial (`mf tutorial). The docs are all here(https://docs.transform.co/docs/overview/metricflow-overview). We’d love contributions on GitHub. We’re adding new Issues to share our roadmap in the coming days, but feel free to open your own.
We’re also opening up a Slack community(https://community.transform.co/metricflow-signup) to talk about the project and, more generally, metric tooling.
Let us know what you think – we’ll be here answering any questions!
What’s the quick pitch for why I should use this instead of Cube or dbt’s metrics layer?
Semantics - The MetricFlow spec allows the construction of a much broader range of metrics with much less expression of logic or duplication of that logic than dbt or Cube.
Performance - MetricFlow generates queries that rivals the optimizations of a skilled Data Engineer and builds pre-aggregated tables similar to Cube while dbt builds a static query from a jinja macro.
Interfaces - Cube has some great interfaces for frontend developers, dbt just generates SQL at this point, and MetricFlow has a Python and CLI . The hosted version, Transform, comes with a SQL and GraphQL Interface but that is beyond the scope of the OSS project.
Semantics
MetricFlow has a less configuration relative to these other frameworks. We accomplish this by choosing abstractions that allow us to handle more on our side at query time through the DataFlow Plan builder. Working with the SQL constructions as a dataflow enables extensions such as non-dw data sources, or using other languages(Python) for some transformations.
The dbt spec is relatively new and requires a few extremely unDRY expressions. The most obvious is the lack of support for joins which means you simply won’t be able to answer most questions unless you build huge tables. There are a few other issues with the abstractions. For example, dimensions are defined multiple times across metrics. A few folks posted more about these challenges in their Github Issue but they’re sticking to their spec. I’m skeptical it will work at any scale.
The Cube concept is similar to Explores in Looker. They’re limiting because you end up with a bunch of representations of small domains within the warehouse and the moment you hit the edge of that domain you need to add a new Cube/Explore. This is not DRY and it’s frustrating. There is also no first-class object for Metrics which means you’re limited to to relatively simple metric types.
Performance
MetricFlow has the flexibility of the DataFlow Plan Builder and builds quite efficient queries. The Materialization feature allows you to build roll up tables programmatically to the data warehouse which could then be used as a low-latency serving layer.
dbt is a jinja macro and generates a static query per metric requested: [https://github.com/dbt-labs/dbt_metrics/blob/main/macros/get.... This macro will be quite hard to optimize for more complicated metric types. We struggled a ton with this before refactoring our framework to allow the manipulation and optimizations of these DataFlow Plans.
Cube is pretty slick on caching, but I know less about their query optimizations. They have some awesome pre-aggregation and caching features. I think this comes from their background in serving frontend interfaces.
Interfaces
MetricFlow supports a Python SDK and our CLI, today. Transform has a few more interfaces (SQL over JDBC, GraphQL, React) that sit outside the scope of this OSS project.
dbt only builds a query in the dbt context today. TBD what the dbt server does but I imagine it will expose a JDBC for paying customers.
Cube seems more focused on building custom data applications but has recently pivoted to the analytics front. I haven’t seen those interfaces in action but I’m curious to learn more there.
In other words, BI tools needs to have a special connector that automatically utilizes MetricFlow Python API (or CLI). What BI tools already can use MetricFlow in this way (open-source part of the project)?
Actually I'm asking about that as a BI tool vendor. We have added an ability to use custom connectors (web API) so potentially this kind of connector can use MetricFlow for SQL generation.
The other options are: - Transform's JDBC can be used to connect to tools that have SQL interfaces like Mode, Hex, Deepnote, etc.: https://docs.transform.co/docs/api/sql/sql-overview - Materializations can be exported as constructed data marts to tools like Tableau / Looker that take in constructed data sources: https://docs.transform.co/docs/metricflow/reference/material...
It seems like you think MetricFlow should be the data mart layer and not just the metrics layer. If that's true...why? Why would I join my fact and dimension tables in metricflow instead of in dbt? One of the value adds of dbt is that it centralizes business logic in a single place. Joins are business logic. The industry seems to be moving towards creating very wide data mart tables in dbt and surfacing them to the semantic layer 1:1, or building the metrics layer on top of them.
That said, you need to support joins, at least in some limited scope, in the semantic metric layer for it to be broadly useful. Consider this scenario - you have your dbt models producing wide tables for reasonable measure/dimension queries, and you have MetricFlow configs for the metric and dimension sets available in your data mart. Now imagine you've also got your finance team hooked up to a Google Sheets connector, and they're looking at revenue and unique customers by sales region. Cool, your wide table has that built in, no joins needed.
But what if they want something new? Let's say they want to know how they're doing against the target addressable market in each country. Should they have to submit a ticket to the data engineering team to add customer.country.market_size to your revenue table? Or should they be able to do "select revenue by customer__country__market_size" and get the report they need?
Our position is that we want to facilitate the latter - people getting what they need and knowing, as long as it's been defined properly in the model, that it's going to produce reasonable results. If your particular organization wants all of those joins run through a data mart ticket queue and surfaced as fully denormalized underlying tables that's fine by us, but most likely that's not what you want. You'd rather have some visibility into the types of joins people are requesting and then build out your data mart to more efficiently serve the requests people have on the ground, while still allowing them to ask new questions of the data without a long development feedback loop.
First, MetricFlow does not currently support MySQL. We launched with support for BigQuery, Redshift, and Snowflake. I have opened an issue to add support for MySQL (and similar issues for other SQL engines are coming): https://github.com/transform-data/metricflow/issues/27
Second, what we call a data source is more similar to a table in a database, rather than the underlying database service itself. Metricflow itself is useful when you're using a single SQL engine - indeed, that's all we support today - but it is most useful when you're in a world where joins are a thing. That said, if you have one big data table you might still find it useful to have declarative metric definitions defined in Metricflow. Suppose, for example, you had a big NoSQL style table filled with JSON objects. You might define a few data sources that normalize those JSON objects into top level elements (identifiers, dimensions, aggregated measures) using the sql_query data source config attribute, and then that'd allow you to support structured queries on the data consumption end while pushing unstructured blobs from your application layer. This will be slow at query time, and only as reliable as the level of discipline exerted in your application development workflow, but it's possible.
Third, if we did support MySQL you'd basically connect to it via standard connection parameters - we have a config file where you can store the required information and then we'll manage the connections for you. However, I'm not familiar with uxwizz, and a quick perusal of their documentation did not turn up how one goes about connecting to the underlying DB. It's likely I just missed this, but at any rate I don't know how it is done. If they don't support standard MySQL client connections you'd need to write an adapter of some kind against whatever DB connection APIs they provide, in which case you'd likely need to roll a custom implementation of MetricFlow's SqlClient interface and initialize the MetricFlowEngine with that.
With data sources, I mean if you have multiple services/products delivering data (e.g. an analytics platform, a CRM, all hosted on different servers).
> uxwizz, and a quick perusal of their documentation did not turn up how one goes about connecting to the underlying DB
UXWizz is self-hosted, so you just have a basic MySQL/MariaDB database that you have full control over (so you can connect remotely with the host/db/username/password you create). This is the database structure: https://docs.uxwizz.com/guides/database-querying
> you have one big data table you might still find it useful to have declarative metric definitions defined in Metricflow
Oh, so this is like saving queries? Why would I write the MetricFlow config instead of saving the SQL query directly? I had look over https://docs.transform.co/docs/metricflow/guides/introductio... but I found the concepts and config file a bit hard to understand (but maybe I'm not in the target audience).
We adopted Looker at $previous_job. Then they got bought by Google, which was great for us as we were becoming a big GCP customer. I strongly encouraged google / looker team to at least open source their LookMl (looker modeling language - equivalent to MQL). They couldn’t figure it out.
This type of metric definition is so empowering for businesses. Not enough engineers grok why this is useful.
Imagine a company that wants to know how much time its users spend on its application, and wants that broken down by country in order to attract investors or something. They probably don’t have one metric for time spent on the application. They have like 12, all of them are different, and although all of them are wrong they each work sort of ok for a given context. To make matters worse, those 12 metrics are defined in different places, computed in different ways, and generated from different data sources. Maybe 3 of them are documented somewhere in the company wiki while the details of the rest live in the heads of 9 current or former employees who are off doing.... something.
That is a mess. Investors don’t care about all that nuance on use cases for time spent measures. Neither does anybody talking to the investors. What they don’t want to hear, when they ask for this simple metric, is “well..... what are you planning to use this for?” On the other hand, things get even worse if somebody grabs the wrong time spent metric and presents that publicly without realizing one of the other 11 was the one they really needed.
It would be better if our hypothetical company had its people define and document a set of consistent metrics and ensure they’re always computed the same way. By centralizing those definitions into a repository (ideally managed by source control) everybody can share the understanding of what a given metric represents. More importantly, they know where to look for pre-existing metric definitions. If this hypothetical company really needs 12 different time spent metrics (they don’t, but bear with me here) then each of those metrics can be named and defined in a way that explains what it’s good for, and they can simply point the makers of their investment pitch at the “baseline_user_time_spent” metric and have done with it.
For any of that to even be possible you need, at a minimum, a place to store metric definitions and a metric layer like MetricFlow that understands those metric definitions, translates them into the relevant queries, and executes them with the required filters and grouping attributes.
- It enables self-serve analytics experiences because it creates objects that business people can interact within pivot-table-like forms. Hundreds of lines of SQL are distilled to simple queries.
- The logic is DRYer / easier to govern than all the repetitive SQL rollups that are required to answer business questions.
- Analysts become more productive write the same stuff less and ask more questions faster.
The idea of limiting duplicated logic is very well understood in the software engineering community and desired in the analytics community but we’re still in the early days. In practice, this is really hard in SQL and the tools we have are too limited.
More specifically, the reason why I get excited about MetricFlow
- We basically built a generalized SQL constructor. It will be able to build performant and legible SQL for complicated requests (things that data engineers describe in hundreds of lines) through simple and consistent query interfaces.
- The way we encapsulate logic requires much fewer lines of yaml/code than most other frameworks and we can do much more with those lines. LookML and previous versions we worked on at Airbnb became quite unruly because of the choices in the abstractions.
- The metric abstraction is flexible and allows us to calculate complicated metrics with only a few lines of yaml. That means we can define metrics like conversion metrics that might take joining two data sources together, deduplicating, filtering to a conversion window, etc. in a single way with a few parameters that reference existing objects.
my wishlist item for 'standard metrics definitions' is for libraries + servers to ship with a spec of what they export
so that if I'm using, for example, a plugin for a reverse proxy, or a twilio verification library, it can install its own metrics and alerts in my dashboard system
It is missing the runtime engine to “interpret” the semantic model and so arbitrary but correct joins among tables and generate the appropriate SQl to realize those arbitrary joins.
If anyone know dbt better and also knows semantic models, please correct.
I'll just say Joins are hard, but if solved elegantly they allow you to do some really interesting stuff. As an example in MetricFlow you can traverse the data graph in your queries. For example if you go `pip install metricflow` and try the tutorial: `mf_tutorial`
You can see what I mean by running queries that traverse the multiple joins to get to other dimensions:
1. Ask for transactions by day by customer `mf query --metrics transactions --dimensions ds,customer --order ds`
2. Ask for transactions by day by customer country `mf query --metrics transactions --dimensions ds,customer__country --order ds`
3. Ask for transactions by day by customer region where we traverse through a country to region mapping `mf query --metrics transactions --dimensions ds,customer__country__region --order ds`
