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LGTM. merged to master
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Test was terminated as "Build execution time has reached the maximum
allowed time for your plan (90 minutes)."
Try to close and reopen the PR to see how the retest goes.
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Retest this please.
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> I'm not sure about the new changes. IMO we are mixing S3 and S4
convention here as per @adrian555.
The implemented code is still conforming to S4 object standard that
`show()` is the
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Github user falaki commented on the issue:
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@adrian555 yes, that looks good. Thank you!
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Thanks @falaki. We are taking out the `print` method out so the requirement
seems clear now. Yes, for Jupyter with IRkernel, two options
`jupyter.rich_display` and jupyter.base_display_func` are
Github user falaki commented on the issue:
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@adrian555 These all great points. My high-level was enabling other
platforms (e.g., Jupyter) to plugin more advanced (custom) functions for
displaying SparkDataFrame. If the framework does not set
Github user adrian555 commented on the issue:
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Another flavor will be this
```r
print.SparkDataFrame <- function(x, ...) {
if ((length(list(...)) > 0) &&
identical(sparkR.conf("spark.sql.repl.eagerEval.enabled", "false")[[1]],
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@falaki trying to understand what you were asking for. I know that `print`
takes some arguments like `digit` and `quote` that can further format the
output. `print` is for S3 object and `show` is
Github user falaki commented on the issue:
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@adrian555 thanks for submitting this. Can we have a config to set the
default `print` function in eager mode. It can default to `show`, but I can
imagine it is useful to make it configurable. For
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> Looks like the test was terminated in the middle, not likely related to
the any code change. Could someone please ask for retest? Thanks.
appveyor one? you need to close and re-open
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Great feature! cc @falaki
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Looks like the test was terminated in the middle, not likely related to the
any code change. Could someone please ask for retest? Thanks.
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@adrian555 The changes look fine to me. Thank you.
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Let's also update the doc of `REPL_EAGER_EVAL_ENABLED` in `SQLConf`. After
this patch, eager evaluation is not only supported in PySpark.
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