Tableau and Snowflake Boolean fields

Hello, gentle reader. It’s been a while. I hope you are well. 🙂

Today, I was helping a colleague with a problem that stems from the fact that the current version of the Snowflake driver in Tableau doesn’t correctly support Boolean fields and therefore returns data from Snowflake BOOLEAN fields as a 1/0 integer value. This means that the field cannot be directly used in a Boolean expression like:


where A and B are both themselves Booleans. So I thought I’d do some digging to understand what was happening under the covers.

For this particular problem, I came up with four different solutions… combinations of using RAWSQL_BOOL to force the result to be interpreted as a Boolean, and simple integer comparisons that return a Boolean result:

  • Create individual Boolean fields for A and B using RAWSQL_BOOL(), then create a third field that ANDs them together.
    • Formula:
      • [A_BOOL] = RAWSQL_BOOL(“A”)
        [B_BOOL] = RAWSQL_BOOL(“B”)
        [A&B] = [A_BOOL] and [B_BOOL]
    • Produced the following SQL:
  • Create a single field that embeds the AND into the RAWSQL_BOOL function. Less flexible than option 1 but maybe better performing?
    • Formula:
      • [A&B] = RAWSQL_BOOL(“A AND B”)
    • Produced the following SQL:
  • Create individual Boolean fields for A and B using an integer comparison, then creating a third field that ANDs them together. My thought here was that integer queries are generally very fast so this might be faster than the RAWSQL approach…
    • Formula:
      • [A_INT] = [A] = 1
        [B_INT] = [B] = 1
        [A&B] = [A_INT] and [B_INT]
    • Produced the following SQL:
      • (CASE WHEN ((“BOOL_TEST”.”A” = 1) AND (“BOOL_TEST”.”B” = 1)) THEN 1 WHEN NOT ((“BOOL_TEST”.”A” = 1) AND (“BOOL_TEST”.”B” = 1)) THEN 0 ELSE NULL END)
  • Create a single field that does an integer comparison for each field and ANDs the results. To be honest, I didn’t expect much difference between this approach and option 3…
    • Formula:
      • [A&B] = [A]=1 AND [B]=1
    • Produced the following SQL:
      • (CASE WHEN ((“BOOL_TEST”.”A” = 1) AND (“BOOL_TEST”.”B” = 1)) THEN 1 WHEN NOT ((“BOOL_TEST”.”A” = 1) AND (“BOOL_TEST”.”B” = 1)) THEN 0 ELSE NULL END)

I put the four examples above into a Tableau worksheet and the following query was run in Snowflake:

SELECT (CASE WHEN (A AND B) THEN 1 WHEN NOT (A AND B) THEN 0 ELSE NULL END) AS “Calculation_ 1822761623506259971”,
(CASE WHEN ((A) AND (B)) THEN 1 WHEN NOT ((A) AND (B)) THEN 0 ELSE NULL END) AS “Calculation_ 1822761623506604036”,
(CASE WHEN ((“BOOL_TEST”.”A” = 1) AND (“BOOL_TEST”.”B” = 1)) THEN 1 WHEN NOT ((“BOOL_TEST”.”A” = 1) AND (“BOOL_TEST”.”B” = 1)) THEN 0 ELSE NULL END) AS “Calculation_ 1822761623507091461”,
(CASE WHEN ((“BOOL_TEST”.”A” = 1) AND (“BOOL_TEST”.”B” = 1)) THEN 1 WHEN NOT ((“BOOL_TEST”.”A” = 1) AND (“BOOL_TEST”.”B” = 1)) THEN 0 ELSE NULL END) AS “Calculation_ 1822761623510069255”,
SUM(“BOOL_TEST”.”C”) AS “sum:C:ok”

Looking at the query profile, the two Boolean approaches produced the same expression in the query profile:


As did the two integer approaches:


Finally, I timed a series of tests against an admittedly small data volume (~100K rows) and the integer logic seemed to come out slightly faster on average – 140ms vs. 166ms over 12 iterations. I’m not convinced that the result is statistically valid, but read into it what you will.

If you want to have a play around with this yourself, here’s the create table statement:


The data for the table and the Tableau workbook can be found in my Dropbox.

Overall, I’m not sure if that helps but it certainly explains what was going on under the covers. It also gives some workaround options until the problem is fixed by Tableau.


The objective of this piece was to discuss how you could use Boolean values in Boolean logic formulae, however, there is one additional piece worth talking about… how to get the field to display as a Boolean true/false value? While the approaches above create fields that are true Booleans in Tableau, there is another approach that doesn’t require creating a calculation, and that is using an alias on the field. You can apply an alias of “true” to the numeric value of “1” and “false” to the numeric value of “0”. This would then display the alias value but the field is still a numeric for the purposes of calculations and such.

I just thought it was worth noting this additional option.

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Snowflake multi-cluster warehouse vs. TabJolt load

One of Snowflake’s key features is something called multi-cluster warehouses.

By default, a virtual warehouse consists of a single cluster of servers that determines the total resources available to the warehouse for executing queries. As queries are submitted to a warehouse, the warehouse allocates resources to each query and begins executing the queries. If sufficient resources are not available to execute all the queries submitted to the warehouse, Snowflake queues the additional queries until the necessary resources become available.

With multi-cluster warehouses, Snowflake supports allocating a larger pool of resources to each warehouse. As the number of concurrent user sessions and/or queries for the warehouse increases, and queries start to queue due to insufficient resources, Snowflake automatically starts additional clusters, up to the maximum number defined for the warehouse.


Similarly, as the load on the warehouse decreases, Snowflake automatically shuts down clusters to reduce the number of running servers and, correspondingly, the number of credits used by the warehouse.

As you can imagine, this capability is very useful to maintain a consistent response time for users in BI and reporting scenarios where varying user loads are common. Imagine that you normally have 5 concurrent users on your system but on Monday mornings, you have a spike of 20 concurrent users, all wanting to view reports for the weekend’s business. A multi-cluster warehouse can ensure that users during the peak load experience the same response times (from the DB that is… saturation of the BI server is a separate factor) as users during non-peak periods.

To show this capability in action, I created a dashboard that queries against about 57M records of Citibike data, hosted in a Snowflake DB. I published this dashboard to Tableau Server and then used TabJolt to simulate a) a single user baseline, and then b) a peak period with multiple concurrent users. Note that caching was turned off both in Tableau Server and in the Snowflake data source to ensure that all queries were actually run in the DB.

You can see this experiment and the results in the following video. Apologies for just providing a download link, but I tried posting this to Vimeo but the video quality was terrible. That’s most likely my fault, but right now this is an easier way to share:


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Snowflake and Tableau – in action!

A few days ago I was invited to present Snowflake as part of a webinar run with our partner BockCorp. Check out the recording of the session here, which includes an overview of the Snowflake architecture as well as a demo showing all the cool capabilities like instant elasticity, semi-structured data support, data sharing and more:

Oh – if you want to jump straight to the demo, it starts around the 29min point. Go ahead, I won’t be offended. 🙂


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Best Practices for Using Tableau with Snowflake

Update 18 July 2018:
The whitepaper has now been released as an official Snowflake whitepaper so I’ve updated the download link to point to the document on the Snowflake website. The new whitepaper is much prettier and has been through editing to clean up all my bad writing habits. Thanks to Vincent Morello and Marta Bright in our content marketing team for all their help in making this happen.

As announced in my last post, since joining Snowflake I’ve been working on a whitepaper that provides best practice guidance for using Tableau with our built-for-the-cloud data warehouse.

Well, I’m pleased to report that it’s done. Or at least, done enough to release. You can download it from here:

I hope you find it useful, and please let me know if you have any feedback or corrections.

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Tableau and Snowflake

Happy New Year everyone!

I’ve been a bit quiet of late. Probably to be expected, what with getting my head around all the new stuff here at Snowflake. Also, properly relaxing over Christmas and summer requires a degree of focus (ah, the joy of the southern hemisphere!). But I’ve not been completely idle. Over the past few weeks I’ve been steadily working on a new whitepaper:


Here’s an overview of the document scope…

  • Introduction
  • What is Tableau?
  • What is Snowflake?
  • What you DON’T have to worry about with Snowflake
  • Creating Efficient Tableau Workbooks
  • Connecting to Snowflake
  • Working with Semi-Structured Data
  • Working with Snowflake Time Travel
  • Working with Snowflake Data Sharing
  • Implementing Role-Based Security
  • Using Custom Aggregations
  • Scaling Snowflake Warehouses
  • Caching
  • Other Performance Considerations
  • Measuring Performance

Of course, it’s turning out to be a lengthy read – it seems I know no other way. 🙂 But believe me, a lot of that is screenshots and SQL. The document is being reviewed at the moment, but I plan to break it into consumable chunks and release material as posts over the next couple of weeks. Maybe here, maybe on the Snowflake or Tableau blogs.

So, keep your eyes peeled…

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Endings and Beginnings

Well it was a bittersweet day on Friday. After 6+ years at Tableau I have decided that it’s time for a new challenge. Tomorrow I start my first day at Snowflake Computing, a company that is revolutionising the cloud analytic database market.


I’m going to continue to blog here and given I still have a deep love for Tableau, some (many) of my posts will continue to be about it and data visualisation in general. However, I’ll also be posting about Snowflake and interesting things I’m learning as I settle in to my new role. Given that a primary use case for Snowflake is BI and analytics, the two topics should be quite complementary.

Thanks for your support and questions over the past few years and I hope you continue to find my ramblings informative.

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Hexbin Scatterplot in Tableau

An interesting tweet came across my Twitter-stream the other day, showing a hexbin scatterplot chart type for Power BI:

Having just presented a session at TC17 on working with dense data where Sarah Battersby and I covered (among other things) hexbinning in Tableau, I was intrigued by this viz type and wondered if it could be created in Tableau. I was a little wary as mixing polygons and points together can be complicated, but I hoped it could be done.

Let’s just say that I’m glad I was bald when I started this exercise because it involved quite a bit of hair-pulling. But after a few hours of trial and error and a well-timed break to go sit in the sun and ruminate, I managed to produce this little beauty:

Hexbin Scatterplot.gif

I started with Alberto Cairo’s Datasaurus dataset – a group of datasets that behave similarly to Anscombe’s quartet. Really I was just being lazy as I had it lying around and therefore didn’t need to mock up my own sample scatterplots. The source data looks like this:


record id































With the data in this format there are two approaches for generating the hexbins – one uses densification to generate the polygon vertex records, and the other generates them through a join to a scaffolding table. I opted to use the scaffolding approach as a) I have a manageable amount of data and b) it makes life easier when you have hexbins that contain just a single point. The scaffold table looks like this:

Point ID

And the join of these tables in Tableau looks like this (the join simulates a Cartesian product of the two tables):

The result of this is 7 rows of data for each point on the scatterplot:

I’ll use one of these (PointID=0) to plot the actual point location, and the other 6 to plot the hexagon shape. I’ve blogged on several occasions on how to generate a dynamic hexbin polygon and we’re going to use the same techniques here:

Generate the hexbin center point:
[HexbinX]: HEXBINX([X]/[Hexbin Size], [Y]/[Hexbin Size]) * [Hexbin Size]
[HexbinY]: HEXBINY([X]/[Hexbin Size], [Y]/[Hexbin Size]) * [Hexbin Size]


Generate a unique identifier for each hexbin. As you may know, I’m an advocate for efficiency so I use a numeric function for this (based on Cantor’s pairing function) instead of a string function:
[HexbinID]: ([HexbinX]^3 + 3*[HexbinX] + 2*[HexbinX]*[HexbinY] + [HexbinY] + [HexbinY]^2)/2


Generate the actual plot points keeping the original location when PointID=0 and using trigonometry to generate the hexagon vertices when PointID=(1..6):
[PointType]: IF [Point ID] = 0 THEN 0 ELSE 1 END
[Angle]: (1.047198 * INDEX())
[PlotX]: IF MIN([PointType]) = 0 THEN MIN([X]) ELSE WINDOW_AVG(MIN([HexbinX])) + [Hexbin Size]*COS([Angle]) END
[PlotY]: IF MIN([PointType]) = 0 THEN MIN([Y]) ELSE WINDOW_AVG(MIN([HexbinY])) + [Hexbin Size]*SIN([Angle]) END

We can now start plotting our viz – first let’s just get the points up:

You can see that the blue marks are the original data points and the orange points are the vertices for the hexagons. Because we want two marks types (a polygon and a point) we need a dual axis chart:

We need to isolate the orange marks on one side and the blue marks on the other. We can’t filter them, so we have to make some clever use of the “hide” function. I duplicated the [PointType] calculation from before so I can use one to colour one axis and the other to colour the other:

We then hide the marks we don’t need on each axis (right-click on the colour swatch in each legend and select “Hide”):

We can now make the hexagon marks on one axis, and circle marks on the other. Tidy up the colours and other formatting:

Finally, we set the axis to be “dual axis”, synchronise and hide the unwanted top axis, and voila:

The last couple of steps I put in were to a) colour the hexbins by the number of points they contain, b) tidy up the tooltips for each mark type, and c) set up a hover action to highlight the elements in a hexbin:

This ended up being quite a challenging viz and required quite a few techniques to get it done. But being able to do it at all reinforces for me that an expressive presentation model that allows you to natively create complex chart types (i.e. the Tableau approach) is faster and more reliable than a model where you are reliant on a developer to write a custom chart widget (i.e. the Power BI model). Even accounting for the trial and error needed to nut out the final successful method, Tableau allowed me to achieve the result much faster than a solution based on coding.

And of course, now that I know how, I can reproduce this solution in minutes.

You can download the workbook from here. Enjoy.


PS. I couldn’t help myself. The workbook now includes solution examples using both the scaffolding and the densification approaches.

Hexbin Scatterplot.png

It was a mental itch that needed scratching.

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