Spark: how to process tree aggregation and statistic
$begingroup$
I have a description of file system in form of a csv:
path;size;inode;type
/folder1/folder1.1;5;1;d
/folder1/folder1.1/file1;10;1;f
/folder1/folder1.1/file2;30;2;f
/folder1/folder1.1/folder1.1.1;5;4;d
/folder1/folder1.1/folder1.1.1/file3;300;5;f
/folder1/folder1.1/folder1.1.1/file4;20;6;f
/folder1/folder1.2;5;7;d
/folder1/file5;30;8;f
/folder1/file6;70;9;f
....
If I put this information in a RRD I will have 4 columns and I could make easy aggregation such as the sum of the size.
However here my goal would be to have a aggregation by folder of different stat such as:
- Recursive file count per folder
- Recursive total size of each folder
- Recursive mean size of file for each folder
If I stop to those 3 values my result could look like this :
path;file count;total size of folder;mean file size in folder
/folder1;6;475;52
/folder1/folder1.1;4;365;73
/folder1/folder1.1/folder1.1.1;2;320;160
/folder1/folder1.2;0;0;0
I have several questions about that kind a treatment :
- Is it relevant to use Spark for such calculation ( I have almost 500 Go of CSV to handle) ?
- If relevant, what would be the best way to approach the problem, should I split my path column and do sub aggregation or should I use method such as
TreeReduceandTreeAggregateor should I use an other way ?
Thanks for any advise on how to handle such problem and feel free to move this question to any Stack site if it belongs elsewhere.
apache-spark
asked 13 hours ago
KiwyKiwy
1136
$endgroup$
add a comment |
$begingroup$
I have a description of file system in form of a csv:
path;size;inode;type
/folder1/folder1.1;5;1;d
/folder1/folder1.1/file1;10;1;f
/folder1/folder1.1/file2;30;2;f
/folder1/folder1.1/folder1.1.1;5;4;d
/folder1/folder1.1/folder1.1.1/file3;300;5;f
/folder1/folder1.1/folder1.1.1/file4;20;6;f
/folder1/folder1.2;5;7;d
/folder1/file5;30;8;f
/folder1/file6;70;9;f
....
If I put this information in a RRD I will have 4 columns and I could make easy aggregation such as the sum of the size.
However here my goal would be to have a aggregation by folder of different stat such as:
- Recursive file count per folder
- Recursive total size of each folder
- Recursive mean size of file for each folder
If I stop to those 3 values my result could look like this :
path;file count;total size of folder;mean file size in folder
/folder1;6;475;52
/folder1/folder1.1;4;365;73
/folder1/folder1.1/folder1.1.1;2;320;160
/folder1/folder1.2;0;0;0
I have several questions about that kind a treatment :
- Is it relevant to use Spark for such calculation ( I have almost 500 Go of CSV to handle) ?
- If relevant, what would be the best way to approach the problem, should I split my path column and do sub aggregation or should I use method such as
TreeReduceandTreeAggregateor should I use an other way ?
Thanks for any advise on how to handle such problem and feel free to move this question to any Stack site if it belongs elsewhere.
apache-spark
asked 13 hours ago
KiwyKiwy
1136
$endgroup$
add a comment |
$begingroup$
I have a description of file system in form of a csv:
path;size;inode;type
/folder1/folder1.1;5;1;d
/folder1/folder1.1/file1;10;1;f
/folder1/folder1.1/file2;30;2;f
/folder1/folder1.1/folder1.1.1;5;4;d
/folder1/folder1.1/folder1.1.1/file3;300;5;f
/folder1/folder1.1/folder1.1.1/file4;20;6;f
/folder1/folder1.2;5;7;d
/folder1/file5;30;8;f
/folder1/file6;70;9;f
....
If I put this information in a RRD I will have 4 columns and I could make easy aggregation such as the sum of the size.
However here my goal would be to have a aggregation by folder of different stat such as:
- Recursive file count per folder
- Recursive total size of each folder
- Recursive mean size of file for each folder
If I stop to those 3 values my result could look like this :
path;file count;total size of folder;mean file size in folder
/folder1;6;475;52
/folder1/folder1.1;4;365;73
/folder1/folder1.1/folder1.1.1;2;320;160
/folder1/folder1.2;0;0;0
I have several questions about that kind a treatment :
- Is it relevant to use Spark for such calculation ( I have almost 500 Go of CSV to handle) ?
- If relevant, what would be the best way to approach the problem, should I split my path column and do sub aggregation or should I use method such as
TreeReduceandTreeAggregateor should I use an other way ?
Thanks for any advise on how to handle such problem and feel free to move this question to any Stack site if it belongs elsewhere.
apache-spark
asked 13 hours ago
KiwyKiwy
1136
$endgroup$
I have a description of file system in form of a csv:
path;size;inode;type
/folder1/folder1.1;5;1;d
/folder1/folder1.1/file1;10;1;f
/folder1/folder1.1/file2;30;2;f
/folder1/folder1.1/folder1.1.1;5;4;d
/folder1/folder1.1/folder1.1.1/file3;300;5;f
/folder1/folder1.1/folder1.1.1/file4;20;6;f
/folder1/folder1.2;5;7;d
/folder1/file5;30;8;f
/folder1/file6;70;9;f
....
If I put this information in a RRD I will have 4 columns and I could make easy aggregation such as the sum of the size.
However here my goal would be to have a aggregation by folder of different stat such as:
- Recursive file count per folder
- Recursive total size of each folder
- Recursive mean size of file for each folder
If I stop to those 3 values my result could look like this :
path;file count;total size of folder;mean file size in folder
/folder1;6;475;52
/folder1/folder1.1;4;365;73
/folder1/folder1.1/folder1.1.1;2;320;160
/folder1/folder1.2;0;0;0
I have several questions about that kind a treatment :
- Is it relevant to use Spark for such calculation ( I have almost 500 Go of CSV to handle) ?
- If relevant, what would be the best way to approach the problem, should I split my path column and do sub aggregation or should I use method such as
TreeReduceandTreeAggregateor should I use an other way ?
Thanks for any advise on how to handle such problem and feel free to move this question to any Stack site if it belongs elsewhere.
apache-spark
apache-spark
asked 13 hours ago
KiwyKiwy
1136
asked 13 hours ago
KiwyKiwy
1136
asked 13 hours ago
KiwyKiwy
1136
asked 13 hours ago
KiwyKiwy
1136
asked 13 hours ago
KiwyKiwy
1136
1136
add a comment |
add a comment |
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