PivotalR is an R package that provides a front-end to PostgreSQL and all PostgreSQL-like databases such as Pivotal Inc.’s Greenplum Database (GPDB), HAWQ. When running on the products of Pivotal Inc., PivotalR utilizes the full power of parallel computation and distributive storage, and thus gives the normal R user access to big data. PivotalR also provides an R wrapper for MADlib. MADlib is an open-source library for scalable in-database analytics. It provides data-parallel implementations of mathematical, statistical and machine-learning algorithms for structured and unstructured data. Thus PivotalR also enables the user to apply machine learning algorithms on big data.
In recent years, Big Data has become an important research topic and a very realistic problem in industry. The amount of data that we need to process is exploding, and the ability of analyzing big data has become the key factor in competition. Big data sets do not fit into computer’s memory and it would be really slow if the big data sets were processed sequentially. On the other hand, most contributed packages of R are still strictly sequential, single machine, and they are restricted to small data sets that can be loaded into memory. As computing shifts irreversibly to parallel architectures and big data, there is a risk for the R community to become irrelevant.
Some efforts have been invested into developing packages that give R users and developers the access to some of the parallel distributive platforms. Some examples include dplyr (Wickham and Francois 2013), RHadoop (Revolution Analytics Company 2012) (including plyrmr, rmr, rhdfs, rhbase), RHIPE (Guha et al. 2012), RHive (NexR 2013), teradataR (Teradata Corporation 2012) etc.
In this paper we introduce the package PivotalR (Pivotal Inc. 2013d), which provides an R front-end with data.frame oriented API for R users to access big data stored in distributive databases or Hadoop distributive file system (HDFS) (The Apache Software Foundation 2013). In this sense, PivotalR is close to the still under development dplyr package, which has a data.frame oriented API and multiple back-ends including several SQL database systems. PivotalR puts more emphasis on machine learning by providing a wrapper for MADlib (Pivotal Inc. 2013c), which is an open-source library of scalable in-database machine learning algorithms. Actually PivotalR offers more than what MADlib has. It adds functionalities that do not exist in MADlib, for example, the support for categorical variables. PivotalR makes it easier to work on big data sets in databases.
This package is especially useful for users who are not familiar with SQL language, because the functions and syntax for the manipulation of tables are very similar to those for data.frame manipulation defined natively in R. Thus the learning curve for the package is very smooth. PivotalR is also very useful for users who are familiar with SQL language, because it brings the graphical and analytical functionalities of R to the processing of big data stored in databases.
PivotalR is contributed to the open-source community by the Predictive Analytics Team at Pivotal Inc. In order to gain the power of distributive storage and parallel computation, the users need to have a distributive database system installed, for example the Greenplum Database or HAWQ. PivotalR also supports the open-source database system PostgreSQL. Therefore PivotalR benefits R users by providing an easy-to-access interface for manipulating data tables stored in PostgreSQL database system and a combination of the powers of R and MADlib, which lets the user directly apply machine learning algorithms on the big data stored in databases.
It is worth mentioning that PivotalR provides the access to data stored on HDFS by supporting HAWQ (Pivotal Inc. 2013b), which is the SQL query engine on HDFS created by Pivotal Inc.
The user does not need to worry about the restriction of memory size even if the data size is very big, because PivotalR minimizes the amount of data transferred between the database and R. The user manipulates the data from R but the data itself stays in the database.
The work flow of using PivotalR is the following. First, the user uses
"db.connect"
to connect to a database. Then, "db.data.frame"
can be
used to create a wrapper for a data table in the database. Minimal
information about the table is kept in the wrapper, and no data is
loaded into the memory. The user can easily operate on the
"db.data.frame"
wrapper object and any operation creates a
"db.Rquery"
object, which is just a wrapper for a series of operations
and contains a SQL query string (see the section of Manipulation of Data
Tables in Database). During this data preparation step, no data is
loaded. In the next step, the user can either call the "lookat"
function to view a sample of the operation result, or call one of the
MADlib wrapper functions to execute a machine learning algorithm. Both
choices initiate computation in the connected database. Usually the
computation result is small and can be loaded into memory for further
processing. In some cases, the result is also big, for example, the
MADlib wrapper for ARIMA produces the residuals for each row of the
data. In such cases, a table is created in the database to store the
result and a wrapper is automatically created in R, and the user can use
the same work flow to analyze the result table.
From the above work flow, one can see that all the actual computation is
done in the database. So the performance depends on the database. For
the Greenplum database or HAWQ, all computation is done in a parallel
and distributive way. Especially the MADlib machine learning functions
are all implemented to fully utilize the parallel and distributive power
of the databases. There are some operations that cannot be done in a
parallel way, for example the ordered aggregate, but they have not been
implemented in PivotalR yet. For the Postgres database, even though
the computation is not in parallel, the user can still use PivotalR to
easily access the data stored in the database, which might be too big to
load into memory. Actually the performance on Postgres is not bad at
all. In our performance tests in Postgres database, PivotalR’s ARIMA
wrapper function for MADlib is faster than R’s own "arima"
function
when applied on large data sets.
It is worth mentioning that the Greenplum database (GPDB) (Pivotal Inc. 2013a) has a community edition, which is free (but not open-source) and has all the functionalities except the technical support from Pivotal Inc.
At the time of writing, the version of PivotalR on CRAN is 0.1.8.
In this paper, we briefly introduce the usage of PivotalR. First in the next section we explain the architecture of PivotalR. Then we use various examples to illustrate the usage and work flow of PivotalR.
As is shown in Fig. 1, PivotalR connects to the database through the RPostgreSQL (Conway et al. 2013) package. However, PivotalR does not directly call the functions in RPostgreSQL. Instead we create an abstraction layer to wrap the functions of RPostgreSQL, and the access to the database is done by calling the functions in the abstraction layer, which then call the functions in RPostgreSQL. This design makes it easier to add supports for other data storage platforms in the future. Right now, PivotalR only supports the connection to PostgreSQL, GPDB (Greenplum Database) and HAWQ. Through HAWQ, PivotalR can access the big data stored on HDFS.
In the future, PivotalR will add supports to other DBMS, Pivotal HD (Hadoop created by Pivotal Inc.) and Hadoop (MapReduce), but we have not decided about how to do that. This is why there are question marks in Fig. 1.
The table operation functions are built upon the abstraction layer for
accessing data. The syntax of these functions is the same as R’s
"data.frame"
operation functions, such as [, [<-, $, $<-
, merge
,
"by"
etc. Arithmetic methods such as +, -, *, /
, logical functions
such as >, <, ==, !=, <=, >=, !, |, &
, math functions such as
log, exp, factorial
etc. are all implemented.
The layers that are built upon the table operations are the MADlib function wrappers and other functions, which will be covered in the subsequent sections.
A graphical user interface using the shiny (RStudio Inc. 2013) package is also implemented. Right now it only provides the graphical interface to the MADlib wrapper functions, but we have the plan to provide the graphical access to all functionalities of PivotalR in the future.
It is worth explaining more about how PivotalR operates on the tables.
Before everything, the user needs to create a "db.data.frame"
object,
which is just a wrapper of the table. Any operation on this object is
translated into an SQL query string. However, the SQL query is not
executed at this point, and is instead stored in an object of class
"db.Rquery"
in R. When the user wants to execute the operation, he can
call the function "preview"
or "lookat"
(or a shorthand "lk"
) to
execute the operation and load part or all of the results into memory to
view. The user can also choose to use "as.db.data.frame"
to execute
the SQL query and save the result into a new table. This design gives
the user the freedom to choose when to load data into memory or create
tables to store intermediate data in the process of a calculation. It
also avoids the risk of accidentally loading big data into memory.
In order to realize the above design, we use a class hierarchy shown in
Fig. 2. PivotalR uses S4 object-oriented programming
extensively. All objects that are related to the data in the database
belong to subclasses of an abstract class named "db.object"
.
As has been mentioned, the class "db.data.frame"
is a wrapper of data
objects stored in the connected database. When it is created by the
command "db.data.frame"
, no actual data stored in the data table in
the database is transferred from the database to R. This object just
contains some basic information about the data object in the database,
such as dimensions and column names. "db.data.frame"
has two
sub-classes, "db.table"
and "db.view"
, which are wrappers for tables
and views respectively.
> library(PivotalR)
> conn.id <- db.connect(port = 14526, dbname = "madlib", host = "localhost")
1
Created a connection to database with ID > x <- db.data.frame("abalone")
The command "db.connect"
in the abstraction layer is used to connect
to one or multiple databases. It has an optional parameter that lets the
user select which package he wants to use to connect to the databases,
although only RPostgreSQL is supported right now. The returned value
of "db.connect"
is an integer which represents the database
connection. PivotalR can connect to multiple databases at the same
time. Thus it is possible to train a machine learning model using the
data from database 1 and then make predictions using the data from
database 2. One can use "db.disconnect"
to disconnect a connection and
"db.list"
to list all active connections.
Any operation on an object of the class "db.data.frame"
produces an
object of the class "db.Rquery"
. An object of the class "db.Rquery"
resides entirely on the R side, and is a wrapper of operations on a
"db.data.frame"
object. Most operations on a "db.Rquery"
object
produces another "db.Rquery"
object. Essentially, a "db.Rquery"
object is just a container of a string of SQL query, which can be viewed
using the command "content"
. The command "as.db.data.frame"
creates
a table in the database using the results of the SQL query contained in
a "db.Rquery"
object and then creates a "db.data.frame"
wrapper that
points to this table.
"preview"
(or its alias "lookat"
and "lk"
) transfers the data from
the database into R’s memory. By default it fetches \(100\) rows of data
from the table in the database and converts the data into a
"data.frame"
in R.
> lookat (x, 10)
id sex length diameter height whole shucked viscera shell rings1 4 M 0.440 0.365 0.125 0.5160 0.2155 0.1140 0.155 10
2 8 F 0.545 0.425 0.125 0.7680 0.2940 0.1495 0.260 16
3 12 M 0.430 0.350 0.110 0.4060 0.1675 0.0810 0.135 10
4 16 M 0.500 0.400 0.130 0.6645 0.2580 0.1330 0.240 12
5 20 M 0.450 0.320 0.100 0.3810 0.1705 0.0750 0.115 9
6 24 F 0.550 0.415 0.135 0.7635 0.3180 0.2100 0.200 9
7 28 M 0.590 0.445 0.140 0.9310 0.3560 0.2340 0.280 12
8 32 F 0.680 0.560 0.165 1.6390 0.6055 0.2805 0.460 15
9 36 M 0.465 0.355 0.105 0.4795 0.2270 0.1240 0.125 8
10 40 M 0.355 0.290 0.090 0.3275 0.1340 0.0860 0.090 9
The MADlib wrappers can be applied to both "db.data.frame"
and
"db.Rquery"
objects. When a MADlib wrapper function is applied to a
"db.Rquery"
object, a temporary table is created using
as.db.data.frame
. MADlib is an in-database library and we have to
convert "db.Rquery"
into an object inside the database so that we
could apply MADlib functions to it. This temporary table is dropped
after the computation is done.
The design of PivotalR minimizes the amount of data that needs to be
transferred between the database and R. The only function that transfers
data from the database is "preview"
.
As has been mentioned, PivotalR overloads many methods that operate on
"data.frame"
, and thus lets the operations on the "db.obj"
objects
mimic those on "data.frame"
as much as possible, with one important
difference discussed below.
> x <- db.data.frame ("abalone")
> dim (x)
1] 4177 10
[> names (x)
1] "id" "sex" "length" "diameter" "height" "whole"
[7] "shucked" "viscera" "shell" "rings"
[> x$rings <- x$rings + 2 / 3
> x$newCol <- (x$rings + x[['length']]) < 10
> content(x)
1] "select \"id\" as \"id\", \"sex\" as \"sex\", \"length\" as \"length\",
[\"diameter\" as \"diameter\", \"height\" as \"height\", \"whole\" as \"whole\",
\"shucked\" as \"shucked\", \"viscera\" as \"viscera\", \"shell\" as \"shell\",
(\"rings\")::double precision + 0.666666666666667 as \"rings\",
(((\"rings\")::double precision + 0.666666666666667)::double precision
+ (\"length\"))::double precision < 10 as \"newCol\" from \"abalone\""
Here "content"
displays the SQL query stored in the "db.Rquery"
. The
quotes inside the SQL query is used to deal with column or table names
with special characters. x
was originally a "db.data.frame"
object,
but was converted to a "db.Rquery"
object by the operation
x$rings <- x$rings + 2 / 3
.
As long as a "key"
is specified when the user calls "db.data.frame"
function, the syntax A[3,4]
can be used. Here the first number "3"
means that the key column has the value of 3
. Therefore,
lk(A[3,4],-1)
may return multiple values. This is different from the
behavior of "data.frame"
, where A[3,4]
returns the element on 3-rd
row and 4-th column. This difference is related to the lack of intrinsic
order in the database tables, which will be discussed in the following.
The rows of a "data.frame"
object have an intrinsic order, which makes
the operations that involve two "data.frame"
objects easier to define.
For example, it is straightforward to define the subtraction of two
columns of two different "data.frame"
objects by matching the order of
the rows as long as they have the same number of rows.
On the other hand, the tables in the database do not have an intrinsic
order for the rows. Thus, most operations like subtraction, addition
etc. can only be defined for columns belonging to the same table.
However, this is usually not a problem. Whenever the user wants to do an
operation that involves two tables, he/she should first call the
function "merge"
to create a new table (actually a "db.Rquery"
object) from the two tables and at the same time explicitly specify how
to match the rows of the two different tables. Then, the user can easily
do operations on the new table. While R’s "data.frame"
implicitly uses
the intrinsic order of the rows to match the rows from two different
data.frames, this needs to be explicitly done for "db.data.frame"
objects. For example,
> x <- db.data.frame("abalone")
> y <- db.data.frame("abalone")
> z <- merge(x, y, by = NULL) # cross join two tables
> names(z)
1] "id_x" "sex_x" "length_x" "diameter_x" "height_x"
[6] "whole_x" "shucked_x" "viscera_x" "shell_x" "rings_x"
[11] "id_y" "sex_y" "length_y" "diameter_y" "height_y"
[16] "whole_y" "shucked_y" "viscera_y" "shell_y" "rings_y"
[> z$sex_x == z$sex_y # This is a db.Rquery object
The data frame abalone
is lazy-loaded in PivotalR. It is used as an
example data here. One can use as.db.data.frame
to transfer this data
set into a table in the database and at the same time create a wrapper
for the table.
> x <- as.db.data.frame(abalone, conn.id=1)
in the data.frame abalone is stored into the table in
The data ! database madlib on localhost
All the subsequent operations will be applied on the table wrapper x
.
A sample of the data is shown in the following, where sort
is used to
create a sorting operation on the original table.
> lookat(sort(x, FALSE, x$id), 10) # load 10 rows of the sorted data
id sex length diameter height whole shucked viscera shell rings1 1 M 0.455 0.365 0.095 0.5140 0.2245 0.1010 0.150 15
2 2 M 0.350 0.265 0.090 0.2255 0.0995 0.0485 0.070 7
3 3 F 0.530 0.420 0.135 0.6770 0.2565 0.1415 0.210 9
4 4 M 0.440 0.365 0.125 0.5160 0.2155 0.1140 0.155 10
5 5 I 0.330 0.255 0.080 0.2050 0.0895 0.0395 0.055 7
6 6 I 0.425 0.300 0.095 0.3515 0.1410 0.0775 0.120 8
7 7 F 0.530 0.415 0.150 0.7775 0.2370 0.1415 0.330 20
8 8 F 0.545 0.425 0.125 0.7680 0.2940 0.1495 0.260 16
9 9 M 0.475 0.370 0.125 0.5095 0.2165 0.1125 0.165 9
10 10 F 0.550 0.440 0.150 0.8945 0.3145 0.1510 0.320 19
Finally, I want to reiterate that all these operations done on
"db.data.frame"
are not executed immediately. Instead a "db.Rquery"
object is produced. The user can convert it into a real table using
as.db.data.frame
.
In the next, we show some examples of MADlib wrapper functions. As is
shown in Fig. 2, all MADlib wrapper functions can be
applied to "db.obj"
objects. Because the connection information is
already contained in "db.obj"
objects, we do not need to specify
"conn.id"
in the MADlib wrapper functions.
First, we show several examples of linear regression. Basically,
PivotalR’s "madlib.lm"
uses the same formula syntax as "lm"
.
Because MADlib’s linear regression supports fitting separate linear
regression models on sub-groups of the data, which are grouped by one or
multiple columns, "madlib.lm"
allows "|"
in the formula, which means
that the model is fit on a subset of the data conditioned on the value
of the variables after "|"
.
> ## fit one different model to each group of data with the same sex
> fit <- madlib.lm(rings ~ . - id | sex, data = x)
sex
has \(3\) distinct values, and thus fit
is a list of 3 elements.
Each element is the linear regression model for a subset of the data.
The mean square error can be easily computed
> ## apply the model to data in another database
> lookat(mean((x$rings - predict(fit, x))^2)) # mean square error
rings_madlib_predict_opr_opr_avg1 4.647291
We can plot a random sample of the fitted values together with the real values, as is shown in Fig. 3. We can also select one model from the fitting result to plot, as is shown in the comments of the following snippets.
> ## plot the result
> ap <- cbind(x$rings, predict(fit, x)) # combine two columns
> plot(lookat(sort(ap, FALSE, NULL), 100)) # plot a random sample
> ## ap <- cbind(x$rings[x$sex == "I"], predict(fit[[1]], x[x$sex == "I",]))
> ## plot(lookat(sort(ap, FALSE, NULL), 100)) # plot a random sample