The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
Sparklyr.flint is a sparklyr extension making Flint time series library functionalities (https://github.com/twosigma/flint) accessible through R.
This extension is currently under active development. It requires the
master branch of sparklyr (i.e., the version from
devtools::install_github("sparklyr/sparklyr", ref = "master")
which is newer that sparklyr 1.2) to be able to load the
com.twosigma:sparklyr-flint_* artifacts correctly from the
https://dl.bintray.com/yl790/maven repository, along with
all transitive dependencies from Maven central.
The com.twosigma:sparklyr-flint_* artifacts contain
minor modifications (mostly changes to the build.sbt file)
needed to ensure Flint time series functionalities work with Spark 2.4
and Spark 3.0. Artifact names and locations are subject to change. They
most likely will be moved to Maven central in future, possibly under a
different group ID as well (TBD).
At the moment, Flint time series functionalities are accessible
through both Spark 2.x and Spark 3.0 via sparklyr, and some commonly
used summarizers such ‘count’ and ‘sum’ are working as expected through
a reasonably intuitive R interface (see example below). Meanwhile there
are still plenty of other Flint functionalities such as EWMA summarizer,
weighted mean, etc that will need similar R interfaces in
sparklyr.flint.
First attach sparklyr.flint package and then connect to
Spark from sparklyr, e.g.,
library(sparklyr)
library(sparklyr.flint)
spark_version <- "2.4.0"
sc <- spark_connect(master = "local", version = spark_version)or alternatively,
spark_version <- "3.0.0"
sc <- spark_connect(master = "local", version = spark_version)since this extension also works with Spark 3.0.
For the purpose of this illustration, we shall create some simple data points such that verifying the correctness of summarized results in all examples below will be an easy exercise for the reader.
df <- tibble::tibble(
t = c(1, 3, 4, 6, 7, 10, 15, 16, 18, 19),
v = c(4, -2, NA, 5, NA, 1, -4, 5, NA, 3)
)
sdf <- copy_to(sc, df, overwrite = TRUE)Next, we shall copy data points from above from a Spark data frame
into a TimeSeriesRDD so that Flint can analyze them:
ts_rdd <- fromSDF(sdf, is_sorted = TRUE, time_unit = "SECONDS", time_column = "t")Alternatively, one can also create a builder object if the same
(is_sorted, time_unit, time_column) settings need to be
applied to multiple Spark data frames or RDDs:
builder <- ts_rdd_builder(sc, is_sorted = TRUE, time_unit = "SECONDS", time_column = "t")
ts_rdd_1 <- builder$fromSDF(sdf_1)
ts_rdd_2 <- builder$fromSDF(sdf_2)
ts_rdd_3 <- builder$fromRDD(rdd_3, schema_of_rdd_3)Let’s say for each time point t (in seconds), we are
interested in a summary all rows within the time span of
[t - 3, t], then we can specify the desired time window in
R as in_past("3s"), and apply various summarizers with this
time window on the TimeSeriesRDD from above.
ts_count <- summarize_count(ts_rdd, window = in_past("3s"))
ts_count %>% collect()should output the total number of rows within each time window:
## # A tibble: 10 x 3
## time v count
## <dttm> <dbl> <dbl>
## 1 1970-01-01 00:00:01 4 1
## 2 1970-01-01 00:00:03 -2 2
## 3 1970-01-01 00:00:04 NaN 3
## 4 1970-01-01 00:00:06 5 3
## 5 1970-01-01 00:00:07 NaN 3
## 6 1970-01-01 00:00:10 1 2
## 7 1970-01-01 00:00:15 -4 1
## 8 1970-01-01 00:00:16 5 2
## 9 1970-01-01 00:00:18 NaN 3
## 10 1970-01-01 00:00:19 3 3ts_count <- summarize_count(ts_rdd, column = "v", window = in_past("3s"))
ts_count %>% collect()should output the total number of values from column v
that are not NULL or NaN within each time
window:
## # A tibble: 10 x 3
## time v v_count
## <dttm> <dbl> <dbl>
## 1 1970-01-01 00:00:01 4 1
## 2 1970-01-01 00:00:03 -2 2
## 3 1970-01-01 00:00:04 NaN 2
## 4 1970-01-01 00:00:06 5 2
## 5 1970-01-01 00:00:07 NaN 1
## 6 1970-01-01 00:00:10 1 1
## 7 1970-01-01 00:00:15 -4 1
## 8 1970-01-01 00:00:16 5 2
## 9 1970-01-01 00:00:18 NaN 2
## 10 1970-01-01 00:00:19 3 2and
ts_sum <- summarize_sum(ts_rdd, column = "v", window = in_past("3s"))
ts_sum %>% collect()should output the sum of values from column v within
each time window, ignoring NULL or NaN
values:
## # A tibble: 10 x 3
## time v v_sum
## <dttm> <dbl> <dbl>
## 1 1970-01-01 00:00:01 4 4
## 2 1970-01-01 00:00:03 -2 2
## 3 1970-01-01 00:00:04 NaN 2
## 4 1970-01-01 00:00:06 5 3
## 5 1970-01-01 00:00:07 NaN 5
## 6 1970-01-01 00:00:10 1 1
## 7 1970-01-01 00:00:15 -4 -4
## 8 1970-01-01 00:00:16 5 1
## 9 1970-01-01 00:00:18 NaN 1
## 10 1970-01-01 00:00:19 3 8These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.