Python: fastest tool to regularly compute matrix cosine/euclidian distances (ElasticSearch/Spark/…)
I have a web application that is used as an API for another application to produce intensive math calculations. Most of these calculations consist in vector-wise similarity calculations, as with scipy.spatial.distance.cdist.
The thing is that these calculations are triggered by an external application, which input a set of vectors for which I want to compute the similarity with some (millions) precomputed vectors. So basically I need a way to perform these similarities as fast as possible. My current solution, which stores the matrix on Redis and loads it every time a new batch of vectors arrives is way to slow. Do you know a solution which is fast that can store big matrices and can operate basic vector operations on it (addition, multiplication, normalization) ?
So far I've found ElasticSearch and Spark MLlib that could be suitable. But before I go for a solution I would like to know if I'm not missing an obvious one that some of you might be aware of.
python elasticsearch matrix apache-spark-mllib large-data
asked Nov 13 '18 at 11:36
debzsuddebzsud
450318
add a comment |
I have a web application that is used as an API for another application to produce intensive math calculations. Most of these calculations consist in vector-wise similarity calculations, as with scipy.spatial.distance.cdist.
The thing is that these calculations are triggered by an external application, which input a set of vectors for which I want to compute the similarity with some (millions) precomputed vectors. So basically I need a way to perform these similarities as fast as possible. My current solution, which stores the matrix on Redis and loads it every time a new batch of vectors arrives is way to slow. Do you know a solution which is fast that can store big matrices and can operate basic vector operations on it (addition, multiplication, normalization) ?
So far I've found ElasticSearch and Spark MLlib that could be suitable. But before I go for a solution I would like to know if I'm not missing an obvious one that some of you might be aware of.
python elasticsearch matrix apache-spark-mllib large-data
asked Nov 13 '18 at 11:36
debzsuddebzsud
450318
did you try to run the computation on the Redis process and not retrieve the matrix all the way to the client side?
– Guy Korland
Nov 20 '18 at 13:57
add a comment |
I have a web application that is used as an API for another application to produce intensive math calculations. Most of these calculations consist in vector-wise similarity calculations, as with scipy.spatial.distance.cdist.
The thing is that these calculations are triggered by an external application, which input a set of vectors for which I want to compute the similarity with some (millions) precomputed vectors. So basically I need a way to perform these similarities as fast as possible. My current solution, which stores the matrix on Redis and loads it every time a new batch of vectors arrives is way to slow. Do you know a solution which is fast that can store big matrices and can operate basic vector operations on it (addition, multiplication, normalization) ?
So far I've found ElasticSearch and Spark MLlib that could be suitable. But before I go for a solution I would like to know if I'm not missing an obvious one that some of you might be aware of.
python elasticsearch matrix apache-spark-mllib large-data
asked Nov 13 '18 at 11:36
debzsuddebzsud
450318
I have a web application that is used as an API for another application to produce intensive math calculations. Most of these calculations consist in vector-wise similarity calculations, as with scipy.spatial.distance.cdist.
The thing is that these calculations are triggered by an external application, which input a set of vectors for which I want to compute the similarity with some (millions) precomputed vectors. So basically I need a way to perform these similarities as fast as possible. My current solution, which stores the matrix on Redis and loads it every time a new batch of vectors arrives is way to slow. Do you know a solution which is fast that can store big matrices and can operate basic vector operations on it (addition, multiplication, normalization) ?
So far I've found ElasticSearch and Spark MLlib that could be suitable. But before I go for a solution I would like to know if I'm not missing an obvious one that some of you might be aware of.
python elasticsearch matrix apache-spark-mllib large-data
python elasticsearch matrix apache-spark-mllib large-data
asked Nov 13 '18 at 11:36
debzsuddebzsud
450318
asked Nov 13 '18 at 11:36
debzsuddebzsud
450318
asked Nov 13 '18 at 11:36
debzsuddebzsud
450318
asked Nov 13 '18 at 11:36
debzsuddebzsud
450318
asked Nov 13 '18 at 11:36
debzsuddebzsud
450318
450318
did you try to run the computation on the Redis process and not retrieve the matrix all the way to the client side?
– Guy Korland
Nov 20 '18 at 13:57
add a comment |
did you try to run the computation on the Redis process and not retrieve the matrix all the way to the client side?
– Guy Korland
Nov 20 '18 at 13:57
did you try to run the computation on the Redis process and not retrieve the matrix all the way to the client side?
– Guy Korland
Nov 20 '18 at 13:57
did you try to run the computation on the Redis process and not retrieve the matrix all the way to the client side?
– Guy Korland
Nov 20 '18 at 13:57
add a comment |
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did you try to run the computation on the Redis process and not retrieve the matrix all the way to the client side?
– Guy Korland
Nov 20 '18 at 13:57