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#datavault, 11 for and 1 against…

The Scene

In March 2021 I proposed a slight modification to the standard satellite table structure and point-in-time (PIT) table snapshot process that would take advantage of Snowflake’s interpretation of a massively parallel platform (MPP) architecture, today I will provide evidence of what these modifications can do.

Please familiarise yourself with both articles before proceeding with the rest of this article.

· Link to March 2021 article:

· Link to why equijoins matter in every analytics platform:

Simulate daily load to four satellites and a common hub table for a six-month period (181 days).

Starting 1 Jan 2021, randomize account number creation and change attributes in satellite tables meaning that some accounts will reference the ghost record in a satellite table to start with while others will start with a reference to actual data. After all a hub table in this model cannot have a business key without at least one of the surrounding satellites supplying that business key!

set my_rec_count = 150000;

Account_ID function: lpad(uniform(1, $my_rec_count, random()), 10, ‘0’) as account_id

Table generator function: table(generator(rowcount => $my_rec_count)))

Build logarithmic PIT tables taking a snapshot of the surrogate hash keys and load dates and logarithmic SnoPIT tables taking a snapshot of the surrogate sequence ids at the following intervals:

- Daily, the only benefit of this PIT is providing an equijoin between the PIT satellite and surrounding satellite tables. Realistically we wouldn’t build a PIT like this!

- Weekly, simulating the business case for having Monday morning data ready for quick access

- Monthly, simulating an end-of-month state of the business objects.

- Current, equijoin to the current state of the business objects

Mod #1: adding autoincrement / identity column to satellite tables

Upon satellite table creation add a new autoincrement/identity column called DV_SID (Data Vault Surrogate ID) with a start number value of zero and a step number increment of 1 (see: Remember, this column carries no relation to any other data vault table, it is however temporal because a new value is assigned for every record and not persisted uniquely for every business key (or unique relationship in the case of a link-satellite table), ala durable key. Every satellite will have its own independent DV_SID.

Insert the Ghost record immediately after table creation; every satellite table must have one. And since it is the first record the DV_SID value will be 0.

Populate with six months’ worth of test data.

Mod #2: building PIT table to return the sequence id

PIT tables are designed to take advantage of physical index-joins between the PIT table itself and its surrounding satellite tables, it is as so described in Dan Linstedt’s “Building a Scalable Data Warehouse with Data Vault 2.0”.

A typical PIT table would look like this.

Since we do not have indexes in Snowflake but instead have a zone-map for every min/max value for every column for every micro-partition we then model the PIT to take advantage of this architecture, ala SnoPIT.

If you noticed, SnoPIT looks more like a Fact table, more on this later!

The Evidence


Of what use is this new structure if we do not provide evidence of its effectiveness…

Exhibit A: Clustering overlap

Snowflake provides the tools to analyse clustering information for your tables

select system$clustering_information(‘<table_name>’, ‘(<column1>, <column2>…)’);


Average overlaps: overlapping micro partitions, high number is bad

Average depth: micro partitions depth, high number is bad

Tablename: sat_card_masterfile, total partitions: 1453, wide and deep table

Thanks to the “randomness” of the hash key value the values appear scattered across micro-partitions. If DV_LOADDATE is considered alone the total constant partition count would be a whopping 1452! But that is expected for a very wide and deep table!

Tablename: sat_card_transaction_header, total partitions: 404, thin and deep table.

In a much thinner table, DV_SID has very similar statistics to the DV_LOADDATE column!

Tablename: sat_bv_card_account_summary, total partitions: 3, thin and shallow table.

If all your tables are this small SnoPIT and even the traditional PIT is not for you!

Tablename: pit_cardaccount_daily, total partitions: 1446

Tablename: snopit_cardaccount_daily, total partitions: 406

DV_SID will have high cardinality, but it is not scattered, it is linear; therefore, when selecting the columns to join on, Snowflake will know which micro-partitions fall into the min/max (zone map) range on both sides of the join query. It also means that no clustering columns should be selected for data vault table definition (DDL) otherwise you could lose the natural clustering in the load order (proven when we tabled DV_LOADDATE)!

Tablename: pit_cardaccount_weekly, total partitions: 204

Tablename: snopit_cardaccount_daily, total partitions: 65

The BV column has the same value per snapshot in each partition!

Tablename: pit_cardaccount_monthly, total partitions: 48

Tablename: snopit_cardaccount_monthly, total partitions: 17

The snapshots have gotten small enough that it doesn’t even matter how the tables are clustered! Recall the table sizes in the sample information above!

More on clustering depth:

Exhibit B: Join performance

PIT table construction involves the use of complicated join criteria, especially in the traditional method (cross-join). PIT tables are built, for this reason, to eliminate the use of LEFT JOIN conditions in data vault queries for the end-user and information marts. If a satellite does not yet have a record for a business key but another satellite table does then an equijoin will not return any records (refer to Ghost record article above that is used to complete the equijoin). It does however mean that this work is left to an automation framework to manage these PIT tables. An alternate pattern would be to run conditional multi-table inserts (see: to related logarithmic PIT table structures, this ensures that there is a single source for all PIT tables and if the specific condition is met then the target table is populated.

Sample code:

This will reduce the PIT build time but what of querying the PIT table in place of the hub or link table, let’s observe some usage statistics. Remember that the intention in Data Vault 2.0 is to deliver Information Marts over PIT tables as views.

To make it a fair comparison between the two methods the following statements were run in a Snowflake session:

· The first statement disables resultset cache for the session (see:

· The second statement flushes out the virtual warehouse cache, it is run after every SQL query statement

Each statement will now utilize remote storage, an apples-to-apples comparison

Select using Daily PIT, both return 27.3m rows

Select using Weekly PITs, both return 3.9m rows

Select using Monthly PITs, both return 900k rows

Right away you can see SnoPIT achieves better pruning and scans fewer bytes than the traditional PIT! Every statistic including execution time is better using SnoPIT! What is also of interest, every query planner graphic for the above queries has a very similar pattern…

a Right Deep Join Tree!

What does this mean?

Simply put, the shape of the join conditions around a centre table resembles a Star Schema, a centre fact table surrounded by dimension tables. To understand this further we’ll briefly discuss physical joins rather than the logical joins everyone is familiar with.

Observe (see:

· Logical Joins

· Physical Joins (see:

o Sort-Merge Joins: sorts one table, stores the sorted intermediate table, sorts the second table, and finally merges the two to form the join result. This results in a Left Deep Join Tree.

o Nested Loop / Index Joins: looks up each row of the smaller table by querying an index of the large table (if both tables have indexes, performs better than Sort-Merge). This results in a Left Deep Join Tree.

o Hash Joins: smaller table is configured in memory as a hash table, performs a row-by-row hash lookup between the larger table and the smaller table. Restricted by memory size. Involves a build phase to load the smaller table into memory (can have a predicate) and a probe phase to find matches in the larger table. When executed with an equijoin predicate, this will result in a Right Deep Join Tree.

Left Deep Join Tree: a join tree in which the left input of every join is the result of a previous join.

Right Deep Join Tree: a join tree in which the right input of every join is the result of a previous join, and the left child of every internal node of a join tree is a table.

You still read from left to write but what happens in a right-deep tree the joins are not physically executing until you’ve reached the last table in the tree, the deep right corner, and note… that is the PIT table! For a deep dive into how this works visit

Closing arguments

We have seen how SnoPIT outperforms traditional PITs and ladies and gentlemen we have also seen evidence of how either PIT structure through its join structure takes advantage of existing data warehouse capabilities. However, constructing PIT tables adds yet another structure for you to manage and thus you only need PIT tables when you are not seeing the query performance you need for your business requirements. As always keep PITs short and thin and SnoPITs produce the same output but are even thinner than traditional PIT tables.

In summary

· SnoPITs are the smaller of the right-deep-join-tree tables, like the PIT

· SnoPITs are thinner than the PIT tables and use a single join condition between itself and adjacent satellite table. The traditional PIT needs two-column joins for each satellite table.

· SnoPITs use a numeric field which is the thinnest data type to join on and thus the fastest to join on, whereas traditional PITs use a binary and datetime field to join on.

· The addition to the satellite table (DV_SID column) is managed by the platform, no additional coding is needed and no lookup to any other table

· SnoPITs’ own columns that reference satellite tables can only increment too unless of course, an entity does not change then the zone map within the SnoPIT becomes less effective. The performance benefit is really in the much larger Satellite tables and their zone maps!

· No clustering of satellite tables can be applied, doing so can break the advantage you get from utilizing Snowflake’s zone maps to find the micro-partitions to solve your star-join query.

· If you are using XTS for timeline correction (see:, note that a timeline correction event does not cause micro-partition overlap! Although the timeline correction is to the timeline itself, the correction event is still seen as a new record in the satellite table (it’s added to the bottom — i.e. logical vs physical timeline correction).

Is this evidence realistic? It’s based on the sampling of fictitious data, realistic loads to a satellite table can vary in frequency, width, and depth of satellite tables. I attempted to simulate this by creating random columns horizontally for two of the satellite tables to get some more statistics. But keep in mind that Snowflake’s micro-partition structure is a hybrid store and achieves excellent compression ratios where an equivalent non-Snowflake table would consume more storage. With this PIT variation as you can see, you will most certainly see joint condition improvements.

How does this methodology using PITs compare to an equivalent query without using PITs at all?

The Verdict

The conclusive evidence… without a PIT table the query ran for 8 minutes and 20 seconds… and the query plan was a Left-Deep Join Tree.

With and without a Daily PIT, all return 27.3m rows

  • All tests were performed using a Medium single node Virtual Warehouse cluster.

SnoPIT is my own variation of the classic PIT table — conceptually designed for Snowflake and the name is an acronym for what it is, Sequence-Number-Only-Point-In-Time table. The screengrabs are from the film “12 Angry men”, the film is noted for referencing each character by number (juror 1 to 12) for almost the entire film, plus it represents what I was trying to achieve in this article, a trial discussing the usage of equijoins in data vault’s PIT table on Snowflake.

The views expressed in this article are that of my own, you should test implementation performance before committing to this implementation. The author provides no guarantees in this regard.

Could you imagine Streams on Views? Stay tuned…



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Patrick Cuba

Patrick Cuba

A Data Vault 2.0 Expert, Snowflake Solution Architect