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https://issues.apache.org/jira/browse/PHOENIX-4925?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Bin Shi updated PHOENIX-4925:
-----------------------------
Description:
As reported, Query compilation (for the sample queries showed below),
especially deriving estimation and generating parallel scans from guide posts,
becomes much slower after we introduced Phoenix Stats.
a. SELECT f1__c FROM MyCustomBigObject__b ORDER BY Pk1__c
b. SELECT f1__c FROM MyCustomBigObject__b WHERE nonpk1__c = ‘x’ ORDER BY Pk1__c
c. SELECT f1__c FROM MyCustomBigObject__b WHERE pk2__c = ‘x’ ORDER BY
pk1__c,pk2__c
d. SELECT f1__c FROM MyCustomBigObject__b WHERE pk1__c = ‘x’ AND nonpk1__c
ORDER BY pk1__c,pk2__c
e. SELECT f1__c FROM MyCustomBigObject__b WHERE pk__c >= 'd' AND pk__c <= 'm'
OR pk__c >= 'o' AND pk__c <= 'x' ORDER BY pk__c // pk__c is the only column to
make the primary key.
By using prefix encoding for guide post info, we have to decode and traverse
guide posts sequentially, which causes time complexity in
BaseResultIterators.getParallelScan(...) to be O( n ) , where n is the total
count of guide posts.
According to PHOENIX-2417, to reduce footprint in client cache and over
transmition, the prefix encoding is used as in-memory and over-the-wire
encoding for guide post info.
We can use Segment Tree to address both memory and performance concerns. The
guide posts are partitioned to k chunks (k=1024?), each chunk is encoded by
prefix encoding and the encoded data is a leaf node of the tree. The inner node
contains summary info (the count of rows, the data size) of the sub tree rooted
at the inner node.
With this tree like data structure, compared to the current data structure, the
increased size (mainly coming from the n/k-1 inner nodes) is ignorable. The
time complexity for queries a, b, c can be reduced to O(m) where m is the total
count of regions; the time complexity for "EXPLAN" queries a, b, c can be
reduced to O(m) too, and if we support "EXPLAIN (ESTIMATE ONLY)", it can even
be reduced to O(1). For queries d and e, the time complexity to find the start
of target scan ranges can be reduced to O(log(n/k)).
The tree can also integrate AVL and B+ characteristics to support partial
load/unload when interacting with stats client cache.
was:
As reported, Query compilation (for the sample queries showed below),
especially deriving estimation and generating parallel scans from guide posts,
becomes much slower after we introduced Phoenix Stats.
a. SELECT f1__c FROM MyCustomBigObject__b ORDER BY Pk1__c
b. SELECT f1__c FROM MyCustomBigObject__b WHERE nonpk1__c = ‘x’ ORDER BY Pk1__c
c. SELECT f1__c FROM MyCustomBigObject__b WHERE pk2__c = ‘x’ ORDER BY
pk1__c,pk2__c
d. SELECT f1__c FROM MyCustomBigObject__b WHERE pk1__c = ‘x’ AND nonpk1__c
ORDER BY pk1__c,pk2__c
e. SELECT f1__c FROM MyCustomBigObject__b WHERE pk__c >= 'd' AND pk__c <= 'm'
OR pk__c >= 'o' AND pk__c <= 'x' ORDER BY pk__c // pk__c is the only column to
make the primary key.
By using prefix encoding for guide post info, we have to decode and traverse
guide posts sequentially, which causes time complexity in
BaseResultIterators.getParallelScan(...) to be O(n) , where n is the total
count of guide posts.
According to PHOENIX-2417, to reduce footprint in client cache and over
transmition, the prefix encoding is used as in-memory and over-the-wire
encoding for guide post info.
We can use Segment Tree to address both memory and performance concerns. The
guide posts are partitioned to k chunks (k=1024?), each chunk is encoded by
prefix encoding and the encoded data is a leaf node of the tree. The inner node
contains summary info (the count of rows, the data size) of the sub tree rooted
at the inner node.
With this tree like data structure, compared to the current data structure, the
increased size (mainly coming from the n/k-1 inner nodes) is ignorable. The
time complexity for queries a, b, c can be reduced to O(m) where m is the total
count of regions; the time complexity for "EXPLAN" queries a, b, c can be
reduced to O(m) too, and if we support "EXPLAIN (ESTIMATE ONLY)", it can even
be reduced to O(1). For queries d and e, the time complexity to find the start
of target scan ranges can be reduced to O(log(n/k)).
The tree can also integrate AVL and B+ characteristics to support partial
load/unload when interacting with stats client cache.
> Use Segment tree to organize Guide Post Info
> --------------------------------------------
>
> Key: PHOENIX-4925
> URL: https://issues.apache.org/jira/browse/PHOENIX-4925
> Project: Phoenix
> Issue Type: Improvement
> Reporter: Bin Shi
> Assignee: Bin Shi
> Priority: Major
>
> As reported, Query compilation (for the sample queries showed below),
> especially deriving estimation and generating parallel scans from guide
> posts, becomes much slower after we introduced Phoenix Stats.
> a. SELECT f1__c FROM MyCustomBigObject__b ORDER BY Pk1__c
> b. SELECT f1__c FROM MyCustomBigObject__b WHERE nonpk1__c = ‘x’ ORDER BY
> Pk1__c
> c. SELECT f1__c FROM MyCustomBigObject__b WHERE pk2__c = ‘x’ ORDER BY
> pk1__c,pk2__c
> d. SELECT f1__c FROM MyCustomBigObject__b WHERE pk1__c = ‘x’ AND nonpk1__c
> ORDER BY pk1__c,pk2__c
> e. SELECT f1__c FROM MyCustomBigObject__b WHERE pk__c >= 'd' AND pk__c <=
> 'm' OR pk__c >= 'o' AND pk__c <= 'x' ORDER BY pk__c // pk__c is the only
> column to make the primary key.
>
> By using prefix encoding for guide post info, we have to decode and traverse
> guide posts sequentially, which causes time complexity in
> BaseResultIterators.getParallelScan(...) to be O( n ) , where n is the total
> count of guide posts.
> According to PHOENIX-2417, to reduce footprint in client cache and over
> transmition, the prefix encoding is used as in-memory and over-the-wire
> encoding for guide post info.
> We can use Segment Tree to address both memory and performance concerns. The
> guide posts are partitioned to k chunks (k=1024?), each chunk is encoded by
> prefix encoding and the encoded data is a leaf node of the tree. The inner
> node contains summary info (the count of rows, the data size) of the sub tree
> rooted at the inner node.
> With this tree like data structure, compared to the current data structure,
> the increased size (mainly coming from the n/k-1 inner nodes) is ignorable.
> The time complexity for queries a, b, c can be reduced to O(m) where m is the
> total count of regions; the time complexity for "EXPLAN" queries a, b, c can
> be reduced to O(m) too, and if we support "EXPLAIN (ESTIMATE ONLY)", it can
> even be reduced to O(1). For queries d and e, the time complexity to find the
> start of target scan ranges can be reduced to O(log(n/k)).
> The tree can also integrate AVL and B+ characteristics to support partial
> load/unload when interacting with stats client cache.
>
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