[jira] [Updated] (TOREE-380) Interpreters should be allowed to send results other than text/plain.
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https://issues.apache.org/jira/browse/TOREE-380?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Ryan Blue updated TOREE-380:
Summary: Interpreters should be allowed to send results other than
text/plain. (was: Interpreters should be allowed to send non-text results.)
> Interpreters should be allowed to send results other than text/plain.
> -
>
> Key: TOREE-380
> URL: https://issues.apache.org/jira/browse/TOREE-380
> Project: TOREE
> Issue Type: Improvement
>Reporter: Ryan Blue
>
> Jupyter allows kernels to send results using different content types, but
> this isn't allowed by Toree for its interpreters. This prevents custom
> display logic. The basic problem is that {{ExecuteOutput}} is a {{String}}
> and not a {{Map[String, String]}} like {{CellMagicOutput}}.
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[jira] [Updated] (TOREE-380) Interpreters should be allowed to send non-text results.
[
https://issues.apache.org/jira/browse/TOREE-380?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Ryan Blue updated TOREE-380:
Description: Jupyter allows kernels to send results using different content
types, but this isn't allowed by Toree for its interpreters. This prevents
custom display logic. The basic problem is that {{ExecuteOutput}} is a
{{String}} and not a {{Map[String, String]}} like {{CellMagicOutput}}. (was:
Jupyter allows kernels to send results using different content types, but this
isn't allowed by Toree for its interpreters. This prevents custom display
logic. The basic problem is that `ExecuteOutput` is a `String` and not a
`Map[String, String]` like `CellMagicOutput`.)
> Interpreters should be allowed to send non-text results.
>
>
> Key: TOREE-380
> URL: https://issues.apache.org/jira/browse/TOREE-380
> Project: TOREE
> Issue Type: Improvement
>Reporter: Ryan Blue
>
> Jupyter allows kernels to send results using different content types, but
> this isn't allowed by Toree for its interpreters. This prevents custom
> display logic. The basic problem is that {{ExecuteOutput}} is a {{String}}
> and not a {{Map[String, String]}} like {{CellMagicOutput}}.
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[jira] [Created] (TOREE-380) Interpreters should be allowed to send non-text results.
Ryan Blue created TOREE-380: --- Summary: Interpreters should be allowed to send non-text results. Key: TOREE-380 URL: https://issues.apache.org/jira/browse/TOREE-380 Project: TOREE Issue Type: Improvement Reporter: Ryan Blue Jupyter allows kernels to send results using different content types, but this isn't allowed by Toree for its interpreters. This prevents custom display logic. The basic problem is that `ExecuteOutput` is a `String` and not a `Map[String, String]` like `CellMagicOutput`. -- This message was sent by Atlassian JIRA (v6.3.15#6346)
[jira] [Created] (TOREE-379) Tab completion doesn't replace partial words
Ryan Blue created TOREE-379: --- Summary: Tab completion doesn't replace partial words Key: TOREE-379 URL: https://issues.apache.org/jira/browse/TOREE-379 Project: TOREE Issue Type: Improvement Reporter: Ryan Blue Tab completion in both notebooks and the console is incorrect and causes the front-end to add the selected option after an incomplete word instead of replacing it. -- This message was sent by Atlassian JIRA (v6.3.15#6346)
[jira] [Created] (TOREE-378) Usability improvements in jupyter console
Ryan Blue created TOREE-378: --- Summary: Usability improvements in jupyter console Key: TOREE-378 URL: https://issues.apache.org/jira/browse/TOREE-378 Project: TOREE Issue Type: Improvement Reporter: Ryan Blue Using Toree with jupyter console has some bugs and unexpected behavior. These include: * IsCompleteHandler sends "incomplete" for indentation * A syntactically complete multi-line block will be run immediately, unlike python which waits for a blank line * Indentation is based on the previous line, but is not anchored -- This message was sent by Atlassian JIRA (v6.3.15#6346)
[jira] [Created] (TOREE-377) When magic fails, the error is swallowed
Ryan Blue created TOREE-377: --- Summary: When magic fails, the error is swallowed Key: TOREE-377 URL: https://issues.apache.org/jira/browse/TOREE-377 Project: TOREE Issue Type: Improvement Reporter: Ryan Blue -- This message was sent by Atlassian JIRA (v6.3.15#6346)
[jira] [Commented] (TOREE-376) Scala interpreter should make Spark SQL implicits available
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https://issues.apache.org/jira/browse/TOREE-376?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15864591#comment-15864591
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Ryan Blue commented on TOREE-376:
-
This is implemented in PR #96.
> Scala interpreter should make Spark SQL implicits available
> ---
>
> Key: TOREE-376
> URL: https://issues.apache.org/jira/browse/TOREE-376
> Project: TOREE
> Issue Type: Improvement
>Reporter: Ryan Blue
>
> The scala interpreter doesn't define Spark SQL functions or implicits that
> are commonly used and available by default in Spark's shell.
> Relevant test cases:
> {code}
> scala> val c = $"test"
> c: org.apache.spark.sql.ColumnName = test
> scala> val df = Seq((1, "a"), (2, "b")).toDF("id", "data")
> df: org.apache.spark.sql.DataFrame = [id: int, data: string]
> scala> df.withColumn("m", map(lit("id"), $"id")).printSchema
> root
> |-- id: integer (nullable = false)
> |-- data: string (nullable = true)
> |-- m: map (nullable = false)
> ||-- key: string
> ||-- value: integer (valueContainsNull = false)
> {code}
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[jira] [Created] (TOREE-376) Scala interpreter should make Spark SQL implicits available
Ryan Blue created TOREE-376:
---
Summary: Scala interpreter should make Spark SQL implicits
available
Key: TOREE-376
URL: https://issues.apache.org/jira/browse/TOREE-376
Project: TOREE
Issue Type: Improvement
Reporter: Ryan Blue
The scala interpreter doesn't define Spark SQL functions or implicits that are
commonly used and available by default in Spark's shell.
Relevant test cases:
{code}
scala> val c = $"test"
c: org.apache.spark.sql.ColumnName = test
scala> val df = Seq((1, "a"), (2, "b")).toDF("id", "data")
df: org.apache.spark.sql.DataFrame = [id: int, data: string]
scala> df.withColumn("m", map(lit("id"), $"id")).printSchema
root
|-- id: integer (nullable = false)
|-- data: string (nullable = true)
|-- m: map (nullable = false)
||-- key: string
||-- value: integer (valueContainsNull = false)
{code}
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