Today, we are going to do Vlookup in Excel with several different formulas, measure their calculation speed and evaluate consistency, so you can choose your winner.
Finding information across different tables is one of the most common tasks in Excel. Regrettably, the classic VLOOKUP function is notorious when it comes to processing power and flexibility. It's no wonder that over the years Excel users have come up with their own solutions such as an INDEX MATCH formula. Luckily, Microsoft has finally realized that VLOOKUP has too many weaknesses and released a more powerful successor - the XLOOKUP function. So, which one is the best to use?
If your tables have only a few dozen rows, then most likely any method will be quick enough, and you won't notice the difference. But if the number of rows is measured in thousands, the correct choice of the function is crucial - the difference in performance can be more than 10 times! So, let's start the timer and see who is the fastest :)
In all the examples, we well be using the following data set:
Our goal is to match the items names and pull the prices from the lookup table into the main table.
For each method, we will enter the formula in C2 and copy it down through C500001, measuring the time it takes Excel to calculate half a million cells.
Naturally, the result will depend on many factors such as your CPU performance, RAM size, Excel version, etc. In this experiment, it is not absolute numbers that matter. It's more important to understand the performance of each formula in comparison, their advantages and drawbacks.
Our examples assume that you have the basic knowledge of the functions, and we won't dwell much on their syntax. Links to in-depth tutorials are included for your convenience.
All tests were carried out on my Dell laptop with Microsoft 365 Apps for business installed; Excel 32-bit, version 2011, build 13415, Beta Channel. The same tests were also performed by my colleague on Excel 64-bit, and some results are drastically different!
When it comes to looking up and retrieving matching data in Excel, the first function that comes to mind is the good old VLOOKUP. In fact, it's not that good as it could be, but we'll talk this a bit later :)
To pull prices from the lookup table (E2:F501) to the main table (A2:B500001), we define the following arguments for our VLOOKUP formula:
The complete formula is:
=VLOOKUP(B2, $E$3:$F$501, 2, FALSE)
The above formula goes to C2, and then we double click the plus sign in the lower right corner to copy the formula across the entire column. At the moment of double-clicking, we run a stopwatch and see that this calculation takes 6.6 seconds.
When doing Vlookup in real worksheets, many people supply the entire columns for table_array to cater for possible additions in the future:
=VLOOKUP(B2, E:F, 2, FALSE)
Does it affect the performance? Yes, it does. It takes 14.2 seconds to calculate the entire column. That was hard to believe, so I double checked. The same result - less than half the speed of the ranges.
Apart from being swift, your formula also needs to be robust and durable, right? Regrettably, VLOOKUP cannot boast of reliability and resilience.
As already mentioned, Excel VLOOKUP has a number of irritating constraints. The most essential ones are:
Calculation time: range - 6.6 seconds; entire columns - 14.2 seconds.
In-depth tutorial: Excel VLOOKUP examples for beginners
For many users, an advanced form of lookup in Excel is the magical INDEX MATCH formula. In generic form, it looks like this:
For our data set, the formula takes this form:
=INDEX($F$2:$F$501, MATCH(B2, $E$2:$E$501, 0))
As with VLOOKUP, please remember to use absolute references for lookup and return ranges to ensure that the formula copies correctly to the below cells.
With both tables on the same sheet, INDEX MATCH performed much slower than VLOOKUP (8.9 against 6.6 seconds).
But if we move the lookup table to another worksheet, the formula starts working much faster (~ 5 seconds), which is better than VLOOKUP.
Anyway, INDEX MATCH makes up the time with a number of vital benefits.
Calculation time: ranges - 8.9 seconds; entire columns - 17.7 seconds; from another sheet - 5.2 seconds.
In-depth tutorial: INDEX MATCH formula in Excel
Here is one more formula to look up vertically in Excel, which is free of many limitations of VLOOKUP:
In our case, the lookup column offset (n) is 0 because we are searching in the first column, so no shift is needed. The return column offset (m) is 1 because the matches are in the second column, and we need to move 1 column to the right to get to them:
=OFFSET($E$2:$F$501, MATCH(B2, OFFSET($E$2:$F$501, 0, 0, ROWS($E$2:$F$501), 1), 0) -1, 1, 1, 1)
Compared to the previous solutions, the formula is too cumbersome, right? However, it is much faster than VLOOKUP or INDEX MATCH. Calculating 500 thousand rows takes less than 3 seconds! On entire columns, OFFSET is just a little slower - 3.5 seconds.
=OFFSET(E:F, MATCH(B2, OFFSET(E:F, 0, 0, ROWS(E:F), 1), 0) -1, 1, 1, 1)
In Excel 64-bit, however, the result is not so impressive - around 7.5 seconds. Why's that? A good question to Microsoft guys :)
Aside from speed, this formula has a few other merits:
Calculation time: ranges - 2.9 seconds; entire columns - 3.5 seconds.
Microsoft 365 subscribers are provided with a new and more powerful function to look up information in their worksheets:
For our purpose, the defaults of the last 3 arguments work just fine, so we specify only the first 3 parameters, which are required. The arguments' names are intuitive, and I believe you can understand the formula without additional explanations:
=XLOOKUP(B2, $E$2:$E$501, $F$2:$F$501)
Compared to traditional VLOOKUP, the XLOOKUP function has many improvements such as:
XLOOKUP is only available in Excel 365. In Excel 2019, Excel 2016 and earlier versions, it is not supported.
And now, let's see how speedy this new function is. 11.2 seconds - quite disappointing :(
What if we use column references instead of ranges?
=XLOOKUP(B2, E:E, F:F)
24.5 seconds. No words… Nearly twice as slow as VLOOKUP.
Calculation time: ranges - 11.2 seconds; entire columns - 24.1 seconds.
Comprehensive tutorial: Excel XLOOKUP function with examples
As you probably know, data in Excel tables can be referenced in a special way - by using table and column names instead of cell addresses. This is called a structured reference, and I wonder if it has any impact on the calculation speed.
To check this, let's convert ranges to tables and re-test our formulas.
For convenience, our tables are named Main_table (A1:C500001) and Lookup_table (E1:F5001).
To create a table reference, start typing the formula in the first cell (C2), select the cells and ranges you want to refer to, and Excel will insert the structured references automatically.
For example, here's how the VLOOKUP formula looks like:
=VLOOKUP([@Item], Lookup_table, 2, FALSE)
A great feature of Excel tables is that as soon as you enter a formula in just one cell, it is immediately populated in all other cells in the same column. Besides, tables are dynamic by nature and expand automatically to include any new data that you type next to a table.
In our table, the VLOOKUP formula calculated in 2.3 seconds, INDEX MATCH in 2.6 seconds, OFFSET and MATCH in 2.7 seconds, and XLOOKUP in 3.3 seconds. As you see, the calculation speed increases significantly compared to ranges.
The formulas are listed below for your reference:
=INDEX(Lookup_table[Price], MATCH([@Item], Lookup_table[Item], 0))
=OFFSET(Lookup_table, MATCH([@Item], OFFSET(Lookup_table, 0, 0, ROWS(Lookup_table), 1), 0) -1, 1, 1, 1)
=XLOOKUP([@Item], Lookup_table[Item], Lookup_table[Price])
Interestingly enough, Excel tables are very fast even with regular references. That is, if you convert only the first range (A1:C500001) to a table and use a normal VLOOKUP formula to pull data from the lookup range, the entire column in the main table will get calculated in about 2.5 seconds!
Calculation speed: from 2.3 to 3.3 seconds depending on the formula.
The groundbreaking change in the Excel 365 calculation engine that occurred in January 2020 has added support for the so-called dynamic arrays. In short, these are resizable arrays that calculate automatically and return values into multiple cells based on a formula entered in one cell.
One of the best things about dynamic arrays is that they can be used with almost any traditional Excel function. For our VLOOKUP formula, it will look like this:
=VLOOKUP(B2:B500001, E2:F500001, 2, FALSE)
The difference with the classic VLOOKUP function is that you supply the entire lookup array for the first argument, not a single lookup value. Because the formula is entered just in one cell, you needn't worry about locking the ranges with absolute references.
As for performance, dynamic arrays work even faster than Excel tables! Half a million cells are filled with the results almost immediately: 1.8 seconds - very impressive!
Other results are listed below:
INDEX MATCH - 4.4 seconds
=INDEX(F2:F501, MATCH(B2:B500001, E2:E501, 0))
XLOOKUP - 7.3 seconds
=XLOOKUP(B2:B500001, E2:E501, F2:F501)
Hmm… XLOOKUP that is supposed to be dynamic by design performs worse than the older functions. Weird!
Calculation speed: from 1.8 to 7.3 seconds depending on the formula.
In-depth tutorial: Excel dynamic arrays, functions and formulas
For the sake of completeness, let's test one more possible solution for our task - Power Query. Of course, it's not quite correct to compare the calculation of formulas with updating the query, but I am just curious which is faster :)
The detailed steps of using Power Query are described in a separate tutorial mentioned below. Here, we will just evaluate the result:
The merged table has loaded from the Power Query Editor into Excel in 8.5 seconds. Unlike formulas, queries do not update automatically. After each change in the source data, you have to update the resulting table manually by clicking the Refresh button on either the Data or Query tab. Our 500,000 rows are refreshed in about 7 seconds. Not bad, but Excel formulas can do better. Considering that setting up a query is far from being a one-click process, this is probably the last method I would use, only if nothing else works.
Performance: loading to Excel 8.5 seconds; refreshing 7.6 seconds
In-depth tutorial: How to combine tables with Excel Power Query
The users of our Ultimate Suite has one more tool in their Excel toolbox to merge two tables based on a common column. Let's see how it compares with Excel's one.
To run the Merge Tables Wizard, click the Merge Two Tables button on the Ablebits Data tab. And then, just follow the steps of the wizard, and it will walk you through the process.
Well, it took the tool about 3 seconds to complete. Not so bad for half a million records!
Having a closer look at the message above, you may notice that not all the matches were found. It does not mean, however, that the tool is flawed. It just lets you know that some items (lookup values) do not exist in the lookup table. The VLOOKUP function returns an #N/A error in this case, while the Merge Tables Wizard leaves a cell blank.
Performance: 3.2 seconds
More information: Merge two tables in Excel
If you carefully read though all the examples, then most likely you have already drawn your own conclusions. If you skipped the details, then you can find a quick summary in this comparison table:
|Function||Calculation speed in seconds|
|Ranges||Entire columns||Table||Dynamic arrays|
Below, there are a few observations that I made based on the test results. Perhaps, they will be useful for you too.
Please keep in mind that these observations are based on my tests in dynamic Excel 365, I didn't have an opportunity to test in other versions. If you did, your comments are welcome and will be greatly appreciated!
Fastest VLOOKUP formula in Excel (.xlsx file, 74MB)
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