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docs: Add summary table of @cacti77

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1ambda 2017-06-27 10:16:18 +09:00
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docs/usage/interpreter/interpreter_binding_mode.md
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@ -62,6 +62,16 @@ So, each Note have their own dedicated session but still it’s possible to shar
## Which mode should I use?
<br/>
Mode | Each notebook... | Benefits | Disadvantages
--- | --- | --- | ---
**shared** | Shares a single Interpreter Group in a single Interpreter Process (JVM) | Low resource utilization and Easy to share data between notebooks | All notebooks are affected if Interpreter Process dies
**scoped** | Has its own Interpreter Group in the same Interpreter Process (JVM) | Less resource utilization than isolated mode | All notebooks are affected if Interpreter Process dies
**isolated** | Has its own Interpreter Process | One notebook not affected directly by other notebooks | Can't share data between notebooks easily
<br/>
Each Interpreter implementation may have different characteristics depending on the back end system that they integrate. And 3 interpreter modes can be used differently.
Let’s take a look how Spark Interpreter implementation uses these 3 interpreter modes, as an example.
Spark Interpreter implementation includes 4 different interpreters in the group: Spark, SparkSQL, Pyspark and SparkR.