indexing

问题 From sql background I know The cardinality of an index is the number of unique values within it. Your database table may have a billion rows in it, but if it only has 8 unique values among those rows, your cardinality is very low. A low cardinality index is not a major efficiency gain. Most SQL indexes are binary search trees (B-Trees). Versus a serial scan of every row in a table to find matching constraints, a B-Tree logarithmically reduces the number of comparisons that have to be made.

问题 With PostgreSQL, I want to use a compound index on three columns A, B, C . B is the created_at datetime, and occasionally I might query without B . What happens if I compound index on (A, B, C) but then query with conditions on A and C , but not B ? (That is, A and C but want it over all time, not just some specific time range?) Is Postgres smart enough to still use the (A, B, C) compound index but just skip B? 回答1: Postgres can use non-leading columns in a b-tree index, but in a far less

问题 With PostgreSQL, I want to use a compound index on three columns A, B, C . B is the created_at datetime, and occasionally I might query without B . What happens if I compound index on (A, B, C) but then query with conditions on A and C , but not B ? (That is, A and C but want it over all time, not just some specific time range?) Is Postgres smart enough to still use the (A, B, C) compound index but just skip B? 回答1: Postgres can use non-leading columns in a b-tree index, but in a far less

问题 I have several queries, most of them being: select * from Blah where col > 0 and select * from Blah where date > current_date Since they're both kind of a range, would an unclustered b+ tree index on col and date be a good idea to speed up the queries? Or a hash index? Or would no index be better? 回答1: Creating an INDEX on the column used in the filter predicate as a date range condition should be useful as it would do a INDEX RANGE SCAN . Here is a demonstration about How to create, display

问题 A simply example of numpy indexing: In: a = numpy.arange(10) In: sel_id = numpy.arange(5) In: a[sel_id] Out: array([0,1,2,3,4]) How do I return the rest of the array that are not indexed by sel_id? What I can think of is: In: numpy.array([x for x in a if x not in a[id]]) out: array([5,6,7,8,9]) Is there any easier way? 回答1: For this simple 1D case, I'd actually use a boolean mask: a = numpy.arange(10) include_index = numpy.arange(4) include_idx = set(include_index) #Set is more efficient, but

问题 A simply example of numpy indexing: In: a = numpy.arange(10) In: sel_id = numpy.arange(5) In: a[sel_id] Out: array([0,1,2,3,4]) How do I return the rest of the array that are not indexed by sel_id? What I can think of is: In: numpy.array([x for x in a if x not in a[id]]) out: array([5,6,7,8,9]) Is there any easier way? 回答1: For this simple 1D case, I'd actually use a boolean mask: a = numpy.arange(10) include_index = numpy.arange(4) include_idx = set(include_index) #Set is more efficient, but

问题 I have a vector in R which contains at least 50.000 reals. The values are ordered from small to large and now I need to split up this vector in different vectors. The vector has to be split up when the difference between two numbers is larger than a given number (say two). Example, data <- c(1,1.1, 1.2, 4, 4.2, 8, 8.9, 9, 9.3); # Then I need the following vectors: x1 <- c(1, 1.1, 1.2); x2 <- c(4, 4.2); x3 <- c(8, 8.9, 9, 9.3); The difficulty is that we don't know the number of needed vectors

问题 I have a vector in R which contains at least 50.000 reals. The values are ordered from small to large and now I need to split up this vector in different vectors. The vector has to be split up when the difference between two numbers is larger than a given number (say two). Example, data <- c(1,1.1, 1.2, 4, 4.2, 8, 8.9, 9, 9.3); # Then I need the following vectors: x1 <- c(1, 1.1, 1.2); x2 <- c(4, 4.2); x3 <- c(8, 8.9, 9, 9.3); The difficulty is that we don't know the number of needed vectors

问题 My data structure is like this: firebase-endpoint/updates/<location_id>/<update_id> each location has many updates that firebase adds as "array" elements. How can I index on the "validFrom" property of each update if the location_id is unknown before insertion into the databse? { "rules": { "updates": { "<location_id>": { // WHAT IS THIS NODE SUPPOSED TO BE? ".indexOn": ["validFrom"] } } } } data structure sample { "71a57e17cbfd0f524680221b9896d88c5ab400b3": { "-KBHwULMDZ4EL_B48-if": { "place

问题 My data structure is like this: firebase-endpoint/updates/<location_id>/<update_id> each location has many updates that firebase adds as "array" elements. How can I index on the "validFrom" property of each update if the location_id is unknown before insertion into the databse? { "rules": { "updates": { "<location_id>": { // WHAT IS THIS NODE SUPPOSED TO BE? ".indexOn": ["validFrom"] } } } } data structure sample { "71a57e17cbfd0f524680221b9896d88c5ab400b3": { "-KBHwULMDZ4EL_B48-if": { "place