Prepared SQL query vs widening WHERE filter
I haven been working with R interfacing my Oracle DB using the DBI package. I read that preparing a query is often a good practice when trying to query the same statment different times.
My question is, assuming infinite RAM to accomodate for the data downloaded, which factors may influence in the different run times between two scenarios: running a prepared query N times or using a WHERE ... BETWEEN filter?
Let's say I have to run a query to analyze some time series information between 2012 and 2018. I have found different download times between running a prepared query for each month between my analysis window and just filtering the whole window.
sql r oracle odbc dbi
asked Nov 14 '18 at 17:05
André OviedoAndré Oviedo
114
add a comment |
I haven been working with R interfacing my Oracle DB using the DBI package. I read that preparing a query is often a good practice when trying to query the same statment different times.
My question is, assuming infinite RAM to accomodate for the data downloaded, which factors may influence in the different run times between two scenarios: running a prepared query N times or using a WHERE ... BETWEEN filter?
Let's say I have to run a query to analyze some time series information between 2012 and 2018. I have found different download times between running a prepared query for each month between my analysis window and just filtering the whole window.
sql r oracle odbc dbi
asked Nov 14 '18 at 17:05
André OviedoAndré Oviedo
114
add a comment |
I haven been working with R interfacing my Oracle DB using the DBI package. I read that preparing a query is often a good practice when trying to query the same statment different times.
My question is, assuming infinite RAM to accomodate for the data downloaded, which factors may influence in the different run times between two scenarios: running a prepared query N times or using a WHERE ... BETWEEN filter?
Let's say I have to run a query to analyze some time series information between 2012 and 2018. I have found different download times between running a prepared query for each month between my analysis window and just filtering the whole window.
sql r oracle odbc dbi
asked Nov 14 '18 at 17:05
André OviedoAndré Oviedo
114
I haven been working with R interfacing my Oracle DB using the DBI package. I read that preparing a query is often a good practice when trying to query the same statment different times.
My question is, assuming infinite RAM to accomodate for the data downloaded, which factors may influence in the different run times between two scenarios: running a prepared query N times or using a WHERE ... BETWEEN filter?
Let's say I have to run a query to analyze some time series information between 2012 and 2018. I have found different download times between running a prepared query for each month between my analysis window and just filtering the whole window.
sql r oracle odbc dbi
sql r oracle odbc dbi
asked Nov 14 '18 at 17:05
André OviedoAndré Oviedo
114
asked Nov 14 '18 at 17:05
André OviedoAndré Oviedo
114
asked Nov 14 '18 at 17:05
André OviedoAndré Oviedo
114
asked Nov 14 '18 at 17:05
André OviedoAndré Oviedo
114
asked Nov 14 '18 at 17:05
André OviedoAndré Oviedo
114
114
add a comment |
add a comment |
1 Answer
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It depends on how the database optimizes your query. Maybe it chooses to optimize with an index when selecting just a single month, maybe it chooses to use a full table scan to retrieve the whole window at once.
Usually I would expect the query to retrieve the entire dataset at once to be more efficient than breaking it up in several parts per month.
Factors that play a role are, among others:
- What percentage of rows in the table are you accessing?
- Are there indexes that can be used?
- Which data was recently accessed (and might be cached)?
- How much data can the database handle/cache in memory?
- Did you use bind variables for the statements?
answered Nov 15 '18 at 13:53
Martin SchapendonkMartin Schapendonk
8,78431324
add a comment |
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1 Answer
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1 Answer
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active
oldest
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oldest
votes
It depends on how the database optimizes your query. Maybe it chooses to optimize with an index when selecting just a single month, maybe it chooses to use a full table scan to retrieve the whole window at once.
Usually I would expect the query to retrieve the entire dataset at once to be more efficient than breaking it up in several parts per month.
Factors that play a role are, among others:
- What percentage of rows in the table are you accessing?
- Are there indexes that can be used?
- Which data was recently accessed (and might be cached)?
- How much data can the database handle/cache in memory?
- Did you use bind variables for the statements?
answered Nov 15 '18 at 13:53
Martin SchapendonkMartin Schapendonk
8,78431324
add a comment |
It depends on how the database optimizes your query. Maybe it chooses to optimize with an index when selecting just a single month, maybe it chooses to use a full table scan to retrieve the whole window at once.
Usually I would expect the query to retrieve the entire dataset at once to be more efficient than breaking it up in several parts per month.
Factors that play a role are, among others:
- What percentage of rows in the table are you accessing?
- Are there indexes that can be used?
- Which data was recently accessed (and might be cached)?
- How much data can the database handle/cache in memory?
- Did you use bind variables for the statements?
answered Nov 15 '18 at 13:53
Martin SchapendonkMartin Schapendonk
8,78431324
add a comment |
It depends on how the database optimizes your query. Maybe it chooses to optimize with an index when selecting just a single month, maybe it chooses to use a full table scan to retrieve the whole window at once.
Usually I would expect the query to retrieve the entire dataset at once to be more efficient than breaking it up in several parts per month.
Factors that play a role are, among others:
- What percentage of rows in the table are you accessing?
- Are there indexes that can be used?
- Which data was recently accessed (and might be cached)?
- How much data can the database handle/cache in memory?
- Did you use bind variables for the statements?
answered Nov 15 '18 at 13:53
Martin SchapendonkMartin Schapendonk
8,78431324
It depends on how the database optimizes your query. Maybe it chooses to optimize with an index when selecting just a single month, maybe it chooses to use a full table scan to retrieve the whole window at once.
Usually I would expect the query to retrieve the entire dataset at once to be more efficient than breaking it up in several parts per month.
Factors that play a role are, among others:
- What percentage of rows in the table are you accessing?
- Are there indexes that can be used?
- Which data was recently accessed (and might be cached)?
- How much data can the database handle/cache in memory?
- Did you use bind variables for the statements?
answered Nov 15 '18 at 13:53
Martin SchapendonkMartin Schapendonk
8,78431324
answered Nov 15 '18 at 13:53
Martin SchapendonkMartin Schapendonk
8,78431324
answered Nov 15 '18 at 13:53
Martin SchapendonkMartin Schapendonk
8,78431324
answered Nov 15 '18 at 13:53
Martin SchapendonkMartin Schapendonk
8,78431324
8,78431324
add a comment |
add a comment |
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