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    <p>This join indeed seems to be misspecified. In general, I think, a self join of a table with single column key is probably always misspecified. Consider the following example :</p> <pre><code>&gt; DT A B 1: 1 5 2: 1 6 3: 1 7 4: 2 8 5: 2 9 &gt; setkey(DT,A) </code></pre> <p>There are 2 unique values of A (1 and 2), but they are repeated in the A column. The key is a single column.</p> <pre><code>&gt; DT[DT] # the long error message &gt; DT[DT, allow.cartesian=TRUE] # **each row** of DT is self joined to DT A B B.1 1: 1 5 5 2: 1 6 5 3: 1 7 5 4: 1 5 6 5: 1 6 6 6: 1 7 6 7: 1 5 7 8: 1 6 7 9: 1 7 7 10: 2 8 8 11: 2 9 8 12: 2 8 9 13: 2 9 9 </code></pre> <p>Is this really the result you need? More likely, the query needs to be changed by adding more columns to the key, doing a <code>by</code> instead, not doing a self join or the hints in the error message.</p> <p>More information about what you need to achieve after the merge (bigger picture) is likely to help.</p> <h2>example of "including j and dropping by (by-without-by) so that j runs for each group to avoid the large allocation" (see error message in question)</h2> <p>The example now in question (covariance) is normally done with matrices rather than data.table. But proceeding anyway to quickly demonstrate, just using it as example data ...</p> <pre><code>require(data.table) country = fread(" Country Product 1 5 1 6 1 7 2 6 2 7 2 8 2 9 ") prod = fread(" Prod1 Prod2 Covariance 5 5 .4 5 6 .5 5 7 .6 5 8 -.3 5 9 -.1 6 6 .3 6 7 .2 6 8 .4 6 9 -.2 7 7 .2 7 8 .1 7 9 .3 8 8 .1 8 9 .6 9 9 .01 ") </code></pre> <p>.</p> <pre><code>country Country Product 1: 1 5 2: 1 6 3: 1 7 4: 2 6 5: 2 7 6: 2 8 7: 2 9 prod Prod1 Prod2 Covariance 1: 5 5 0.40 2: 5 6 0.50 3: 5 7 0.60 4: 5 8 -0.30 5: 5 9 -0.10 6: 6 6 0.30 7: 6 7 0.20 8: 6 8 0.40 9: 6 9 -0.20 10: 7 7 0.20 11: 7 8 0.10 12: 7 9 0.30 13: 8 8 0.10 14: 8 9 0.60 15: 9 9 0.01 </code></pre> <p>.</p> <pre><code>setkey(country,Country) country[country,{print(.SD);print(i.Product)}] # included j to demonstrate j running for each row of i. Just printing to demo. Product 1: 5 2: 6 3: 7 [1] 5 Product 1: 5 2: 6 3: 7 [1] 6 Product 1: 5 2: 6 3: 7 [1] 7 Product 1: 6 2: 7 3: 8 4: 9 [1] 6 Product 1: 6 2: 7 3: 8 4: 9 [1] 7 Product 1: 6 2: 7 3: 8 4: 9 [1] 8 Product 1: 6 2: 7 3: 8 4: 9 [1] 9 Empty data.table (0 rows) of 2 cols: Country,Product </code></pre> <p>.</p> <pre><code>setkey(prod,Prod1,Prod2) country[country,prod[J(i.Product,Product),Covariance,nomatch=0]] Country Prod1 Prod2 Covariance 1: 1 5 5 0.40 2: 1 5 6 0.50 3: 1 5 7 0.60 4: 1 6 6 0.30 5: 1 6 7 0.20 6: 1 7 7 0.20 7: 2 6 6 0.30 8: 2 6 7 0.20 9: 2 6 8 0.40 10: 2 6 9 -0.20 11: 2 7 7 0.20 12: 2 7 8 0.10 13: 2 7 9 0.30 14: 2 8 8 0.10 15: 2 8 9 0.60 16: 2 9 9 0.01 country[country, prod[J(i.Product,Product),Covariance,nomatch=0][ ,mean(Covariance),by=Country] Country V1 1: 1 0.3666667 2: 2 0.2010000 </code></pre> <p>This doesn't match the desired result due to not doubling the off diagonal. But hopefully this is enough to demonstrate that particular suggestion in the error message in the question and you can take it from here. Or use <code>matrix</code> rather than <code>data.table</code> for covariance type work.</p>
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    1. POJoin results in more than 2^31 rows (internal vecseq reached physical limit)
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    1. USMatt Dowle
    1. USMatt Dowle
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    1. COThis is exactly the result I need. I am trying to study interactions between prods that share a tag. This is what leads me to the error message above. Your help would be appreciated.
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    2. COB and B.1 in your example give me pairs of those prods, for which i can estimate correlations etc.
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    3. COSo it looks like we are back to tweaking data.table.
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