Data Management from a Theory of Constraints Perspective

November 14th, 2013

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When I read Eliyahu Goldratt’s the Goal in Grad School, one of the key things that stuck with me is that there’s always a bottleneck and that the process only moves as fast as the bottleneck allows it to move. The Theory of Constraints methodology posits three key measures for an organization: Throughput, Inventory, and Operational Expense. Sitting down to think about this, I reasoned that we could use those same metrics to measure the total cost of data for copies, clones and backups.

For every bit of data that enters the door through Production, we could offer that the Throughput represents the data generated to create or update the copies, clones and backups. Inventory could represent the number of copies of each bit of production data that sits on a copy, clone, or backup. And, Operational Expense represents all of the Labor and Resources spent creating and transmitting that data from Production to its final resting place.

When expressed in these terms, the compelling power of thin cloning is clear. Let me show you what I mean by a little Thought Experiment:

Thought Experiment

If I had a fairly standard application with 1 Production Source, 8 downstream copies, and a 4 week – Weekly Full / Daily Incremental backup scheme and a plan to refresh the downstream systems on average 1/week, what would the metrics look like with and without thin cloning?

TOC Metrics for Cloned/Copies/Backed-up Data

Throughput
8 * Daily Change Rate of Production

Inventory
Copies
8 * Full Size of Production
+
Backups
4 * Full Size of Prod (1/week for 4 Weeks)
24 * Daily Change Rate of Production (6/week for 4 weeks)

Operational Expense
Copies
8 shipments and applications of change data / day
+
Backups
1 Backup Operation/Day

With Delphix thin cloning, these metrics change significantly. The shared data footprint eliminates most of the shipment and application and redundancy. So:

TOC Metrics for Cloned/Copies/Backed-up Data using thin clones

Throughput
1 * Daily Change Rate of Production

Why?
Change is shipped and applied to the shared footprint once.

Inventory
Copies
1 * Full Size of Production (Before being Compressed)
+
Backups
28 * Daily Change Rate of Production

Why?
A full copy is only ever taken once. (Otherwise, it is incremental forever.)

Operational Expense
Copies
1shipments and applications of change data / day
+
Backups
0 Backup Operation/Day

Why?
Since change is applied to the common copy, backups are just redundant operations.

So what?

The thought experiment reflects what we see every day with Delphix. The Throughput of data that has to move through a system is significantly less (8x less in our experiment). And, it gets relatively more efficient as you scale. The Inventory of data that has to be kept by the system is not driven by the number of copies, but rather is driven by the change rate and the amount of change kept. Unless you are completely flopping over your copies downstream (in which case you have different problems), this also gets relatively more efficient as you scale. And finally, when it comes to Operational Expense, you’re not just getting more efficient, you’re actually eliminating whole classes of effort and radically simplifying others.

The bottom line here is that Data has been THE BIG BOTTLENECK for a long time in our applications. And, with thin cloning from Delphix, you’ve finally got the power to take control of that bottleneck, measure the true impact, and reduce your Total Cost of Data by delivering the right data to the right person at a fraction of the cost you pay today.

 


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+ eight = 11