Comments (4)
I really like this idea. And I think it's closer to what @mcuadros was thinking of as well. /cc @marnovo @campoy
from jgit-spark-connector.
Yes, looks more straightforward.
Two questions regarding the comparison to the current:
from sourced.engine import Engine
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
spark = SparkSession.builder \
.master("local[*]").appName("Examples") \
.getOrCreate()
engine = Engine(spark, "/repositories")
- In practical terms, what is lost in terms of flexibility from the current approach?
- Is it more likely that abstracting this away from the user might break something under certain circumstances or make it harder to debug in case of a problem?
from jgit-spark-connector.
Is not possible to do the proposed API changes on the engine because, when you start a spark-shell, notebook, and so on, the spark session is provided. Because of that, we wouldn't create a new session inside the Engine wrapper, we should use the existing one.
For the other hand, use standard python console is another of the several ways that we could run the engine (JVM application, scala-shell, pyspark-shell, jupyter scala and python notebooks, zeppelin notebooks, and so on.)
If it's really important support the standard python shell right now, we can do that, but I think we already have a lot of methods to use the engine in a really simple way.
add pyspark to PYTHONPATH during installation
I think this is a really bad practice. Change environment variables during a dependency download? I don't think that is even possible using pipy.
from jgit-spark-connector.
@ajnavarro I think your argument makes sense. Closing this issue now.
from jgit-spark-connector.
Related Issues (20)
- Return of org.eclipse.jgit.errors.RevWalkException: Walk failure HOT 2
- [feature request] Handy handling of bad siva-files HOT 2
- Engine fails on UAST when run spark-shell with --packages argument in cluster mode HOT 6
- Modify requirements file, Pyspark version HOT 2
- Add a configuration flag to skip siva read errors
- [Change] Files not cleaned in /tmp folder on Spark Workers when using apollo HOT 2
- commits filtering counterintuitive behaviour HOT 1
- Engine fails to extract UASTs on actual Spark cluster HOT 5
- Latest version of Docker image engine-jupyter missing bblfsh HOT 6
- Expose Bblfsh UAST extraction errors in Engine
- Jupyter example notebook doesn't work out-of-the-box
- Docker Hub 'latest' image doesn't point to master Dockerfile. HOT 2
- Missing dependencies for pyspark in README HOT 6
- Build and test process leave the repository in dirty state for git describe HOT 3
- Engine fails on siva files from pipeline-staging cluster HOT 14
- Update README with EOL
- "standard" repository format does not work HOT 7
- ReferencesDataFrame files property HOT 4
- WARN Repository: close() called when useCnt is already zero
- WARN CommitIterator: missing object HOT 1
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from jgit-spark-connector.