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new d2436ef updated doc and package
d2436ef is described below
commit d2436efa99476b49ae1e5107243b494c6d93ba33
Author: AlexanderSaydakov <[email protected]>
AuthorDate: Fri Jun 14 16:15:39 2019 -0700
updated doc and package
---
META.json | 8 ++--
README.md | 126 +++++++++++++++++++++++++++++++++++++++++++++++++--
datasketches.control | 2 +-
3 files changed, 126 insertions(+), 10 deletions(-)
diff --git a/META.json b/META.json
index 51852f5..98a2747 100644
--- a/META.json
+++ b/META.json
@@ -1,7 +1,7 @@
{
"name": "datasketches",
"abstract": "approximate algorithms for big data analysis",
- "version": "1.1.0",
+ "version": "1.2.0",
"maintainer": [
"Alexander Saydakov <[email protected]>",
"Sketches User List <[email protected]>"
@@ -31,11 +31,11 @@
},
"resources": {
"bugtracker": {
- "web": "https://github.com/DataSketches/sketches-postgres/issues";
+ "web":
"https://github.com/apache/incubator-datasketches-postgresql/issues";
},
"repository": {
- "url": "https://github.com/DataSketches/sketches-postgres.git";,
- "web": "https://github.com/DataSketches/sketches-postgres";,
+ "url":
"https://github.com/apache/incubator-datasketches-postgresql.git";,
+ "web": "https://github.com/apache/incubator-datasketches-postgresql";,
"type": "git"
}
},
diff --git a/README.md b/README.md
index cedf49d..ed9bf43 100644
--- a/README.md
+++ b/README.md
@@ -1,10 +1,12 @@
-Module for PostgreSQL to support approximate algorithms based on the
Datasketches core library sketches-core-cpp.
-See https://datasketches.github.io/ for details.
+Module for PostgreSQL to support approximate algorithms based on the
Datasketches core library datasketches-cpp.
+See [Datasketches documentation](https://datasketches.github.io/) for details.
-This module currently supports two sketches:
+This module currently supports the following sketches:
- CPC (Compressed Probabilistic Counting) sketch - very compact (when
serialized) distinct-counting sketch
+- Theta sketch - distinct counting with set operations (intersection, a-not-b)
- KLL float quantiles sketch - for estimating distributions: quantile, rank,
PMF (histogram), CDF
+- Frequent strings sketch - capture the heaviest items (strings) by count or
by some other weight
<h1>Examples</h1>
@@ -14,7 +16,7 @@ Suppose 100 million random integer values uniformly
distributed in the range fro
Exact count distinct:
- $ time psql test -c "select count(distinct id) from random_ints_100m;"
+ $ time psql test -c "select count(distinct id) from random_ints_100m"
count
----------
63208457
@@ -24,7 +26,7 @@ Exact count distinct:
Approximate count distinct:
- $ time psql test -c "select cpc_sketch_distinct(id) from
random_ints_100m;"
+ $ time psql test -c "select cpc_sketch_distinct(id) from
random_ints_100m"
cpc_sketch_distinct
---------------------
63423695.9451363
@@ -45,6 +47,60 @@ Merging sketches:
-------------------------
3.00024414612919
+<h2>Distinct counting with Theta sketch</h2>
+
+See above for the exact distinct count of 100 million random integers
+
+Approximate distinct count:
+
+ $ time psql test -c "select theta_sketch_distinct(id) from
random_ints_100m"
+ theta_sketch_distinct
+ -----------------------
+ 64593262.4373193
+ (1 row)
+
+ real 0m19.701s
+
+Note that the above one-off distinct count is just to show the basic usage.
Most importantly, the sketch can be used as an "additive" distinct count metric
in a data cube.
+
+Aggregate union:
+
+ create table theta_sketch_test(sketch theta_sketch);
+ insert into theta_sketch_test select theta_sketch_build(1);
+ insert into theta_sketch_test select theta_sketch_build(2);
+ insert into theta_sketch_test select theta_sketch_build(3);
+ select theta_sketch_get_estimate(theta_sketch_union(sketch)) from
theta_sketch_test;
+ theta_sketch_get_estimate
+ ---------------------------
+ 3
+
+Non-aggregate set operations:
+
+ create table theta_set_op_test(sketch1 theta_sketch, sketch2
theta_sketch);
+ insert into theta_set_op_test select theta_sketch_build(1),
theta_sketch_build(1);
+ insert into theta_set_op_test select theta_sketch_build(1),
theta_sketch_build(2);
+
+ select theta_sketch_get_estimate(theta_sketch_union(sketch1, sketch2))
from theta_set_op_test;
+ theta_sketch_get_estimate
+ ---------------------------
+ 1
+ 2
+ (2 rows)
+
+ select theta_sketch_get_estimate(theta_sketch_intersection(sketch1,
sketch2)) from theta_set_op_test;
+ theta_sketch_get_estimate
+ ---------------------------
+ 1
+ 0
+ (2 rows)
+
+ select theta_sketch_get_estimate(theta_sketch_a_not_b(sketch1,
sketch2)) from theta_set_op_test;
+ theta_sketch_get_estimate
+ ---------------------------
+ 0
+ 1
+ (2 rows)
+
<h2>Estimating quanitles, ranks and histograms with KLL sketch</h2>
Table "normal" has 1 million values from the normal distribution with mean=0
and stddev=1.
@@ -91,3 +147,63 @@ The ARRAY[-2, -1, 0, 1, 2] of 5 split points defines 6
intervals (bins): (-inf,-
In this simple example we know the value of N since we constructed this
sketch, but in a general case sketches are merged across dimensions of data
hypercube, so the vale of N is not known in advance.
Note that the normal distribution was used just to show the basic usage. The
sketch does not make any assumptions about the distribution.
+
+<h2>Frequent strings</h2>
+
+Consider a numeric Zipfian distribution with parameter alpha=1.1 (high skew)
+and range of 2<sup>13</sup>, so that the number 1 has the highest frequency,
+the number 2 appears substantially less frequently and so on.
+Suppose zipf\_1p1\_8k\_100m table has 100 million random values drawn from such
+a distribution, and the values are converted to strings.
+
+Suppose the goal is to get the most frequent strings from this table. In
+terms of the frequent items sketch we have to chose a threshold. Let's try
+to capture values that repeat more than 1 million times, or more than 1% of
+the 100 million entries in the table. According to the [error
table](https://datasketches.github.io/docs/Frequency/FrequentItemsErrorTable.html),
+frequent items sketch of size 2<sup>9</sup> must capture all values more
+frequent then about 0.7% of the input.
+
+The following query is to build a sketch with lg_k=9 and get results with
+estimated weight above 1 million using "no false negatives" policy.
+The output format is: value, estimate, lower bound, upper bound.
+
+ $ time psql test -c "select
frequent_strings_sketch_result_no_false_negatives(frequent_strings_sketch_build(9,
value), 1000000) from zipf_1p1_8k_100m"
+ frequent_strings_sketch_result_no_false_negatives
+ ---------------------------------------------------
+ (1,15328953,15209002,15328953)
+ (2,7156065,7036114,7156065)
+ (3,4578361,4458410,4578361)
+ (4,3334808,3214857,3334808)
+ (5,2608563,2488612,2608563)
+ (6,2135715,2015764,2135715)
+ (7,1801961,1682010,1801961)
+ (8,1557433,1437482,1557433)
+ (9,1368446,1248495,1368446)
+ (10,1216532,1096581,1216532)
+ (11,1098304,978353,1098304)
+ (11 rows)
+
+ real 0m38.178s
+
+Here is an equivalent exact computation:
+
+ $ time psql test -c "select value, weight from (select value, count(*)
as weight from zipf_1p1_8k_100m group by value) t where weight > 1000000 order
by weight desc"
+ value | weight
+ -------+----------
+ 1 | 15328953
+ 2 | 7156065
+ 3 | 4578361
+ 4 | 3334808
+ 5 | 2608563
+ 6 | 2135715
+ 7 | 1801961
+ 8 | 1557433
+ 9 | 1368446
+ 10 | 1216532
+ 11 | 1098304
+ (11 rows)
+
+ real 0m18.362s
+
+In this particular case the exact computation happens to be faster. This is
+just to show the basic usage. Most importantly, the sketch can be used as an
"additive" metric in a data cube, and can be easily merged across dimensions.
\ No newline at end of file
diff --git a/datasketches.control b/datasketches.control
index 72634af..6fdc30f 100644
--- a/datasketches.control
+++ b/datasketches.control
@@ -1,6 +1,6 @@
# Datasketches module
comment = 'Aggregation functions and data types for approximate algorithms.'
-default_version = '1.0.0'
+default_version = '1.2.0'
relocatable = true
module_pathname = '$libdir/datasketches'
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