Patent Application
20240118791
The disclosed implementations relate generally to data visualization and more specifically to systems, methods, and user interfaces to prepare and curate data for use by a data visualization application.
Data visualization applications enable a user to understand a data set visually, including distribution, trends, outliers, and other factors that are important to making business decisions. Some data sets are very large or complex, and include many data fields. Various tools can be used to help understand and analyze the data, including dashboards that have multiple data visualizations. However, data frequently needs to manipulated or massaged to put it into a format that can be easily used by data visualization applications. Sometimes various ETL (Extract/Transform/Load) tools are used to build usable data sources.
There are two dominant models in the ETL and data preparation space today. Data flow style systems focus the user on the operations and flow of the data through the system, which helps provide clarity on the overall structure of the job, and makes it easy for the user to control those steps. These systems, however, generally do a poor job of showing the user their actual data, which can make it difficult for users to actually understand what is or what needs to be done to their data. These systems can also suffer from an explosion of nodes. When each small operation gets its own node in a diagram, even a moderately complex flow can turn into a confusing rat's nest of nodes and edges.
On the other hand, Potter's Wheel style systems present the user with a very concrete spreadsheet-style interface to their actual data, and allow the user to sculpt their data through direct actions. While users are actually authoring a data flow in these systems, that flow is generally occluded, making it hard for the user to understand and control the overall structure of their job.
Disclosed implementations have features that provide the benefits of both Data flow style systems and Potter's Wheel style systems, and go further to make it even easier for a user build a data flow. The disclosed data preparation applications describe data flows, but collapse nodes into larger groups that better represent the high-level actions users wish to take. The design of these nodes utilizes direct action on actual data, guided by statistics and relevant visualizations at every step.
In accordance with some implementations, a computer system prepares data for analysis. The computer system includes one or more processors, memory, and one or more programs stored in the memory. The programs are configured for execution by the one or more processors. The programs display a user interface for a data preparation application. The user interface includes a data flow pane, a tool pane, a profile pane, and a data pane. The data flow pane displays a node/link flow diagram that identifies data sources, operations, and output data sets. The tool pane includes a data source selector that enables users to add data sources to the flow diagram, includes an operation palette that enables users to insert nodes into the flow diagram for performing specific transformation operations, and a palette of other flow diagrams that a user can incorporate into the flow diagram. The profile pane displays schemas corresponding to selected nodes in the flow diagram, including information about data fields and statistical information about data values for the data fields and enables users to modify the flow diagram by interacting with individual data elements. The data pane displays rows of data corresponding to selected nodes in the flow diagram, and enables users to modify the flow diagram by interacting with individual data values.
In some implementations, the information about data fields displayed in the profile pane includes data ranges for a first data field.
In some implementations, in response to a first user action on a first data range for the first data field in the profile pane, a new node is added to the flow diagram that filters data to the first data range.
In some implementations, the profile pane enables users to map the data ranges for the first data field to specified values, thereby adding a new node to the flow diagram that performs the user-specified mapping.
In some implementations, in response to a first user interaction with a first data value in the data pane, a node is added to the flow diagram that filters the data to the first data value.
In some implementations, in response to a user modification of a first data value of a first data field in the data pane, a new node is added to the flow diagram that performs the modification to each row of data whose data value for the first data field equals the first data value.
In some implementations, in response to a first user action on a first data field in the data pane, a node is added to the flow diagram that splits the first data field into two or more separate data fields.
In some implementations, in response to a first user action in the data flow pane to drag a first node to the tool pane, a new operation is added to the operation palette, the new operation corresponding to the first node.
In some implementations, the profile pane and data pane are configured to update asynchronously as selections are made in the data flow pane.
In some implementations, the information about data fields displayed in the profile pane includes one or more histograms that display distributions of data values for data fields.
In accordance with some implementations, a method executes at an electronic device with a display. For example, the electronic device can be a smart phone, a tablet, a notebook computer, or a desktop computer. The method implements any of the computer systems described herein.
In some implementations, a non-transitory computer readable storage medium stores one or more programs configured for execution by a computer system having one or more processors, memory, and a display. The one or more programs include instructions for implementing a system that prepares data for analysis as described herein.
Thus, methods, systems, and graphical user interfaces are disclosed that enable users to analyze, prepare, and curate data.
For a better understanding of the aforementioned systems, methods, and graphical user interfaces, as well as additional systems, methods, and graphical user interfaces that provide data visualization analytics and data preparation, reference should be made to the Description of Implementations below, in conjunction with the following drawings in which like reference numerals refer to corresponding parts throughout the figures.
Reference will now be made to implementations, examples of which are illustrated in the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that the present invention may be practiced without requiring these specific details.
The graphical user interface 100 also includes a data visualization region 112. The data visualization region 112 includes a plurality of shelf regions, such as a columns shelf region 120 and a rows shelf region 122. These are also referred to as the column shelf 120 and the row shelf 122. As illustrated here, the data visualization region 112 also has a large space for displaying a visual graphic. Because no data elements have been selected yet, the space initially has no visual graphic. In some implementations, the data visualization region 112 has multiple layers that are referred to as sheets.
The computing device 200 includes a user interface 206 comprising a display device 208 and one or more input devices or mechanisms 210. In some implementations, the input device/mechanism includes a keyboard. In some implementations, the input device/mechanism includes a “soft” keyboard, which is displayed as needed on the display device 208, enabling a user to “press keys” that appear on the display 208. In some implementations, the display 208 and input device/mechanism 210 comprise a touch screen display (also called a touch sensitive display).
In some implementations, the memory 214 includes high-speed random access memory, such as DRAM, SRAM, DDR RAM or other random access solid state memory devices. In some implementations, the memory 214 includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices. In some implementations, the memory 214 includes one or more storage devices remotely located from the CPU(s) 202. The memory 214, or alternately the non-volatile memory device(s) within the memory 214, comprises a non-transitory computer readable storage medium. In some implementations, the memory 214, or the computer readable storage medium of the memory 214, stores the following programs, modules, and data structures, or a subset thereof:
In some instances, the computing device 200 stores a data prep application 250, which can be used to analyze and massage data for subsequent analysis (e.g., by a data visualization application 222).
Each of the above identified executable modules, applications, or sets of procedures may be stored in one or more of the previously mentioned memory devices, and corresponds to a set of instructions for performing a function described above. The above identified modules or programs (i.e., sets of instructions) need not be implemented as separate software programs, procedures, or modules, and thus various subsets of these modules may be combined or otherwise re-arranged in various implementations. In some implementations, the memory 214 stores a subset of the modules and data structures identified above. Furthermore, the memory 214 may store additional modules or data structures not described above.
Although
This interface provides a user with multiple streamlined, coordinated views that help the user to see and understand what they need to do. This novel user interface presents users with multiple views of their flow and their data to help them not only take actions, but also discover what actions they need to take. The flow diagram in the flow pane 303 combines and summarizes actions, making the flow more readable, and is coordinated with views of actual data in the profile pane 304 and the data pane 305. The data pane 305 provides representative samples of data at every point in the logical flow, and the profile pane provides histograms of the domains of the data.
In some implementations, the Menu Bar 301 has a File menu with options to create new data flow specifications, save data flow specifications, and load previously created data flow specifications. In some instances, a flow specification is referred to as a flow. A flow specification describes how to manipulate input data from one or more data sources to create a target data set. The target data sets are typically used in subsequent data analysis using a data visualization application.
In some implementations, the Left-Hand Pane 302 includes a list of recent data source connections as well as a button to connect to a new data source.
In some implementations, the Flow Pane 303 includes a visual representation (flow diagram or flow) of the flow specification. In some implementations, the flow is a node/link diagram showing the data sources, the operations that are performed, and target outputs of the flow.
Some implementations provide flexible execution of a flow by treating portions of flow as declarative queries. That is, rather than having a user specify every computational detail, a user specifies the objective (e.g., input and output). The process that executes the flow optimizes plans to choose execution strategies that improve performance. Implementations also allow users to selectively inhibit this behavior to control execution.
In some implementations, the Profile Pane 304 displays the schema and relevant statistics and/or visualizations for the nodes selected in the Flow Pane 303. Some implementations support selection of multiple nodes simultaneously, but other implementations support selection of only a single node at a time.
In some implementations, the Data Pane 305 displays row-level data for the selected nodes in the Flow Pane 303.
In some implementations, a user creates a new flow using a “File->New Flow” option in the Menu Bar. Users can also add data sources to a flow. In some instances, a data source is a relational database. In some instances, one or more data sources are file-based, such as CSV files or spreadsheet files. In some implementations, a user adds a file-based source to the flow using a file connection affordance in the left-hand pane 302. This opens a file dialog that prompts the user to choose a file. In some implementations, the left hand pane 302 also includes a database connection affordance, which enables a user to connect to a database (e.g., an SQL database).
When a user selects a node (e.g., a table) in the Flow Pane 303, the schema for the node is displayed in the Profile Pane 304. In some implementations, the profile pane 304 includes statistics or visualizations, such as distributions of data values for the fields (e.g., as histograms or pie charts). In implementations that enable selection of multiple nodes in the flow pane 303, schemas for each of the selected nodes are displayed in the profile pane 304.
In addition, when a node is selected in the Flow Pane 303, the data for the node is displayed in the Data Pane 305. The data pane 305 typically displays the data as rows and columns.
Implementations make it easy to edit the flow using the flow pane 303, the profile pane 304, or the data pane 305. For example, some implementations enable a right click operation on a node/table in any of these three panes and add a new column based on a scalar calculation over existing columns in that table. For example, the scalar operation could be a mathematical operation to compute the sum of three numeric columns, a string operation to concatenate string data from two columns that are character strings, or a conversion operation to convert a character string column into a date column (when a date has been encoded as a character string in the data source). In some implementations, a right-click menu (accessed from a table/node in the Flow Pane 303, the Profile Pane 304, or the Data Pane 305) provides an option to “Create calculated field . . . ” Selecting this option brings up a dialog to create a calculation. In some implementations, the calculations are limited to scalar computations (e.g., excluding aggregations, custom Level of Detail calculations, and table calculations). When a new column is created, the user interface adds a calculated node in the Flow Pane 303, connects the new node to its antecedent, and selects this new node. In some implementations, as the number of nodes in the flow diagram gets large, the flow pane 303 adds scroll boxes. In some implementations, nodes in the flow diagram can be grouped together and labeled, which is displayed hierarchically (e.g., showing a high-level flow initially, with drill down to see the details of selected nodes).
A user can also remove a column by interacting with the Flow Pane 303, the Profile Pane 304, or the Data Pane 305 (e.g., by right clicking on the column and choosing the “Remove Column” option. Removing a column results in adding a node to the Flow Pane 303, connecting the new node appropriately, and selecting the new node.
In the Flow Pane 303, a user can select a node and choose “Output As” to create a new output dataset. In some implementations, this is performed with a right click. This brings up a file dialog that lets the user select a target file name and directory (or a database and table name). Doing this adds a new node to the Flow Pane 303, but does not actually create the target datasets. In some implementations, a target dataset has two components, including a first file (a Tableau Data Extract or TDE) that contains the data, and a corresponding index or pointer entry (a Tableau Data Source or TDS) that points to the data file.
The actual output data files are created when the flow is run. In some implementations, a user runs a flow by choosing “File->Run Flow” from the Menu Bar 301. Note that a single flow can produce multiple output data files. In some implementations, the flow diagram provides visual feedback as it runs.
In some implementations, the Menu Bar 301 includes an option on the “File” menu to “Save” or “Save As,” which enables a user to save the flow. In some implementations, a flow is saved as a “.loom” file. This file contains everything needed to recreate the flow on load. When a flow is saved, it can be reloaded later using a menu option to “Load” in the “File” menu. This brings up a file picker dialog to let the user load a previous flow.
In some implementations, the left-hand pane 312 includes a data source palette/selector, which includes affordances for locating and connecting to data. The set of connectors includes extract-only connectors, including cubes. Implementations can issue custom SQL expressions to any data source that supports it.
The left-hand pane 312 also includes an operations palette, which displays operations that can be placed into the flow. This includes arbitrary joins (of arbitrary type and with various predicates), union, pivot, rename and restrict column, projection of scalar calculations, filter, aggregation, data type conversion, data parse, coalesce, merge, split, aggregation, value replacement, and sampling. Some implementations also support operators to create sets (e.g., partition the data values for a data field into sets), binning (e.g., grouping numeric data values for a data field into a set of ranges), and table calculations (e.g., calculate data values (e.g., percent of total) for each row that depend not only on the data values in the row, but also other data values in the table).
The left-hand pane 312 also includes a palette of other flows that can be incorporated in whole or in part into the current flow. This enables a user to reuse components of a flow to create new flows. For example, if a portion of a flow has been created that scrubs a certain type of input using a combination of 10 steps, that 10 step flow portion can be saved and reused, either in the same flow or in completely separate flows.
The flow pane 313 displays a visual representation (e.g., node/link flow diagram) 323 for the current flow. The Flow Pane 313 provides an overview of the flow, which serves to document the process. In many existing products, a flow is overly complex, which hinders comprehension. Disclosed implementations facilitate understanding by coalescing nodes, keeping the overall flow simpler and more concise. As noted above, as the number of nodes increases, implementations typically add scroll boxes. The need for scroll bars is reduced by coalescing multiple related nodes into super nodes, which are also called container nodes. This enables a user to see the entire flow more conceptually, and allows a user to dig into the details only when necessary. In some implementations, when a “super node” is expanded, the flow pane 313 shows just the nodes within the super node, and the flow pane 313 has a heading that identifies what portion of the flow is being display. Implementations typically enable multiple hierarchical levels. A complex flow is likely to include several levels of node nesting.
As described above, the profile pane 314 includes schema information about the data at the currently selected node (or nodes) in the flow pane 313. As illustrated here, the schema information provides statistical information about the data, such as a histogram 324 of the data distribution for each of the fields. A user can interact directly with the profile pane to modify the flow 323 (e.g., by selecting a data field for filtering the rows of data based on values of that data field). The profile pane 314 also provides users with relevant data about the currently selected node (or nodes) and visualizations that guide a user's work. For example, histograms 324 show the distributions of the domains of each column. Some implementations use brushing to show how these domains interact with each other.
An example here illustrates how the process is different from typical implementations by enabling a user to directly manipulate the data in a flow. Consider two alternative ways of filtering out specific rows of data. In this case, a user wants to exclude California from consideration. Using a typical tool, a user selects a “filter” node, places the filter into the flow at a certain location, then brings up a dialog box to enter the calculation formula, such as “state_name< >“CA”. In disclosed implementations here, the user can see the data value in the profile pane 314 (e.g., the column State Code 326 shows the field value “California” and a histogram bar for California indicates how many rows have that field value) and in the data pane 315 (e.g., individual rows with “California” as the value for state_name). In some implementations, the user can right click on “California” in the list of state names in the Profile Pane 314 (or in the Data Pane 315), and choose “Exclude” from a drop down. The user interacts with the data itself, not a flow element that interacts with the data. Implementations provide similar functionality for calculations, joins, unions, aggregates, and so on. Another benefit of the approach is that the results are immediate. When “California” is filtered out, the filter applies immediately. If the operation takes some time to complete, the operation is performed asynchronously, and the user is able to continue with work while the job runs in the background.
The data pane 315 displays the rows of data corresponding to the selected node or nodes in the flow pane 313. Each of the columns 325 corresponds to one of the data fields. A user can interact directly with the data in the data pane to modify the flow 323 in the flow pane 313. A user can also interact directly with the data pane to modify individual field values. In some implementations, when a user makes a change to one field value, the user interface applies the same change to all other values in the same column whose values (or pattern) match the value that the user just changed. For example, if a user changed “WA” to “Washington” for one field value in the State Code data column, some implementations update all other “WA” values to “Washington” in the same column. Some implementations go further to update the column to replace any state abbreviations in the column to be full state names (e.g., replacing “OR” with “Oregon”). In some implementations, the user is prompted to confirm before applying a global change to an entire column. In some implementations, a change to one value in one column can be applied (automatically or pseudo-automatically) to other columns as well. For example, a data source may include both a state for residence and a state for billing. A change to formatting for states can then be applied to both.
The sampling of data in the data pane 315 is selected to provide valuable information to the user. For example, some implementations select rows that display the full range of values for a data field (including outliers). As another example, when a user has selected nodes that have two or more tables of data, some implementations select rows to assist in joining the two tables. The rows displayed in the data pane 315 are selected to display both rows that match between the two tables as well as rows that do not match. This can be helpful in determining which fields to use for joining and/or to determine what type of join to use (e.g., inner, left outer, right outer, or full outer).
Different nodes within the flow diagram 323 perform different tasks, and thus the node internal information is different. In addition, some implementations display different information depending on whether or not a node is selected. For example, an unselected node includes a simple description or label, whereas a selected node displays more detailed information. Some implementations also display status of operations. For example, some implementations display nodes within the flow diagram 323 differently depending on whether or not the operations of the node have been executed. In addition, within the operations palette, some implementations display operations differently depending on whether or not they are available for use with the currently selected node.
A flow diagram 323 provides an easy, visual way to understand how the data is getting processed, and keeps the process organized in a way that is logical to a user. Although a user can edit a flow diagram 323 directly in the flow pane 313, changes to the operations are typically done in a more immediate fashion, operating directly on the data or schema in the profile pane 314 or the data pane 315 (e.g., right clicking on the statistics for a data field in the profile pane to add or remove a column from the flow).
Rather than displaying a node for every tiny operation, users are able to group operations together into a smaller number of more significant nodes. For example, a join followed by removing two columns can be implemented in one node instead of three separate nodes.
Within the flow pane 313, a user can perform various tasks, including:
The profile pane 314 provides a quick way for users to figure out if the results of the transforms are what they expect them to be. Outliers and incorrect values typically “pop out” visually based on comparisons with both other values in the node or based on comparisons of values in other nodes. The profile pane helps users ferret out data problems, regardless of whether the problems are caused by incorrect transforms or dirty data. In addition to helping users find the bad data, the profile pane also allows direct interactions to fix the discovered problems. In some implementations, the profile pane 314 updates asynchronously. When a node is selected in the flow pane, the user interface starts populating partial values (e.g., data value distribution histograms) that get better as time goes on. In some implementations, the profile pane includes an indicator to alert the user whether is complete or not. With very large data sets, some implementations build a profile based on sample data only.
Within the profile pane 314, a user can perform various tasks, including:
The data pane 315 provides a way for users to see and modify rows that result from the flows. Typically, the data pane selects a sampling of rows corresponding to the selected node (e.g., a sample of 10, 50, or 100 rows rather than a million rows). In some implementations, the rows are sampled in order to display a variety of features. In some implementations, the rows are sampled statistically, such as every nth row.
The data pane 315 is typically where a user cleans up data (e.g., when the source data is not clean). Like the profile pane, the data pane updates asynchronously. When a node is first selected, rows in the data pane 315 start appearing, and the sampling gets better as time goes on. Most data sets will only have a subset of the data available here (unless the data set is small).
Within the data pane 315, a user can perform various tasks, including:
A node specific pane displays information that is particular for a selected node in the flow. Because a node specific pane is not needed most of the time, the user interface typically does not designate a region with the user interface that is solely for this use. Instead, a node specific pane is displayed as needed, typically using a popup that floats over other regions of the user interface. For example, some implementations use a node specific pane to provide specific user interfaces for joins, unions, pivoting, unpivoting, running Python scripts, parsing log files, or transforming a JSON objects into tabular form.
The Data Source Palette/Chooser enables a user to bring in data from various data sources. In some implementations, the data source palette/chooser is in the left-hand pane 312. A user can perform various tasks with the data source palette/chooser, including:
In many cases, users invoke operations on nodes in the flow based on user interactions with the profile pane 314 and data pane 315, as illustrated above. In addition, the left hand pane 312 provides an operations palette, which allows a user to invoke certain operations. For example, some implementations include an option to “Call a Python Script” in the operations palette. In addition, when users create nodes that they want to reuse, they can save them as available operations on the operations palette. The operations palette provides a list of known operations (including user defined operations), and allows a user to incorporate the operations into the flow using user interface gestures (e.g., dragging and dropping).
Some implementations provide an Other Flow Palette/Chooser, which allows users to easily reuse flows they've built or flows other people have built. The other flow palette provides a list of other flows the user can start from, or incorporate. Some implementations support selecting portions of other flows in addition to selecting entire flows. A user can incorporate other flows using user interface gestures, such as dragging and dropping.
The node internals specify exactly what operations are going on in a node. There is sufficient information to enable a user to “refactor” a flow or understand a flow more in more detail. A user can view exactly what is in the node (e.g., what operations are performed), and can move operations out of the node, into another node.
Some implementations include a project model, which allows a user to group together multiple flows into one “project” or “workbook.” For complex flows, a user may split up the overall flow into more understandable components.
In some implementations, operations status is displayed in the left-hand pane 312. Because many operations are executed asynchronously in the background, the operations status region indicates to the user what operations are in progress as well as the status of the progress (e.g., 1% complete, 50% complete, or 100% complete). The operations status shows what operations are going on in the background, enables a user to cancel operations, enables a user to refresh data, and enables a user to have partial results run to completion.
A flow, such as the flow 323 in
A flow abstraction like the one shown in
Some implementations here span the range from fully-declarative queries to imperative programs. Some implementations utilize an internal abstract query language (AQL) and a Federated Evaluator. By default, a flow is interpreted as a single declarative query specification whenever possible. This declarative query is converted into AQL and handed over to a Query Evaluator, which ultimately divvies up the operators, distributes, and executes them. In the example above in
When a user wants to control the actual execution order of the flow (e.g., for performance reasons), the user can pin an operation. Pinning tells the flow execution module not to move operations past that point in the plan. In some instances, a user may want to exercise extreme control over the order temporarily (e.g., during flow authoring or debugging). In this case, all of the operators can be pinned, and the flow executed in exactly the order the user has specified.
Note that not all flows are decomposable into a single AQL query, as illustrated in
The profile pane 314 provides distribution data for each of the columns, including the state column 410, as illustrated in
In
The Join Experience 442 includes a toolbar area 448 with various icons, as illustrated in
In some implementations, selection of the “show keys” icon 454 causes the interface to identify which data columns are keys or parts of a key that consists of multiple data fields. Some implementations include a data/metadata toggle icon 456, which toggles the display from showing the information about the data to showing information about the metadata. In some implementations, the data is always displayed, and the metadata icon 456 toggles whether or not the metadata is displayed in addition to the data. Some implementations include a data grid icon 458, which toggles display of the data grid 315. In
On the left of the join experience 442 is a set of join controls, including a specification of the join type 464. As is known in the art, a join is typically a left outer join, an inner join, a right outer join, or a full outer join. These are shown graphically by the join icons 464. The current join type is highlighted, but the user can change the type of the join by selecting a different icon.
Some implementations provide a join clause overview 466, which displays both the names of the fields on both sides of the join, as well as histograms of data values for the data fields on both sides of the join. When there are multiple data fields in the join, some implementations display all of the relevant data fields; other implementations include a user interface control (not shown) to scroll through the data fields in the join. Some implementations also include an overview control 468, which illustrates how many rows from each of the tables are joined based on the type of join condition. Selection of portions within this control determines what is displayed in the profile pane 314 and the data grid 315.
Disclosed implementations support many features that assist in a variety of scenarios. Many of the features have been described above, but some of the following scenarios illustrate the features.
Alex works in IT, and one of his jobs is to collect and prepare logs from the machines in their infrastructure to produce a shared data set that is used for various debugging and analysis in the IT organization.
The machines run Windows, and Alex needs to collect the Application logs. There is already an agent that runs every night and dumps CSV exports of the logs to a shared directory; each day's data are output to a separate directory, and they are output with a format that indicates the machine name. A snippet from the Application log is illustrated in
This has some interesting characteristics:
Alex creates a flow that reads in all of the CSV files in a given directory, and performs a jagged union on them (e.g., create a data field if it exists in at least one of the CSV files, but when the same data field exists in two or more of the CSV files, create only one instance of that data field). The CSV input routine does a pretty good job reading in five columns, but chokes on the quotes in the sixth column, reading them in as several columns.
Alex then:
All of this is accomplished through direct action on the data in the data pane 315, but results in logic being inserted into the flow in the flow pane 313.
Alex then drags his target data repository into the flow pane, and wires up the output to append these records to a cache that will contain a full record of his logs.
Finally, Alex's flow queries this target dataset to find the set of machines that reported the previous day, compares this to today's machines, and outputs an alert to Alex with a list of expected machines that did not report.
Note that Alex could have achieved the same result in different ways. For example:
Alex knows that the machines should report overnight, so what Alex does the first thing every morning is run his flow. He then has the rest of the morning to check up on machines that did not report.
Bonnie works for an insurance company, and would like to pull in the Fatality Analysis Reporting System (FARS) data as a component of her analysis. The FARS data is available via FTP, and Bonnie needs to figure out how to get it and piece it together. She decides to do this using the data prep application 250.
Bonnie takes a look at the set of formats that FARS publishes in, and decides to use DBF file. These DBF files are spread around the FTP site and available only in compressed ZIP archives. Bonnie explores a tree view and selects the files she wishes to download. As the data is downloading, Bonnie begins the next step in her flow. She selects the collection of files and chooses “Extract,” which adds a step to unzip the files into separate directories labeled by the year.
As the data starts to come in, Bonnie starts to sees problems:
Bonne starts with the accident table, which is present in all years. She chooses the files, right clicks, and chooses “Union,” which performs a jagged union and preserves the columns. She repeats this with the other three tables present in all years, and then for the remaining tables. When she's done, her flow's final stage produces 19 separate tables.
Once she has this, she tries piecing the data together. It looks like the common join key should be a column called ST_CASE, but just by looking at the profile pane for the accident table, she can tell this isn't a key column anywhere. ST_CASE isn't a key, but by clicking on years, she can easily see that there is only one ST_CASE per year. Together, year and ST_CASE look like a good join key.
She starts with the person table. Before she can join on this, she needs the year in each of her tables, and it isn't there. But since the file paths have the year, she can select this data in the data pane and choose “Extract as new column.” The system infers the correct pattern for this, and extracts the year for each row. She then selects both tables in her flow, selects the year and ST_CASE columns in one, and drags them to the other table, creating a join.
Now that she has the keys, she continues to create joins to flatten out the FARS data. When she's done, she publishes the data as a TDE (Tableau Data Extract) to her Tableau Server so her team can use it.
Colin is another employee at in the same department as Bonnie. Some people are trying to use the data Bonnie's flow produces, but it includes lots of cryptic values. Finding that Bonnie has moved on to another company, they turn to Colin.
Looking at the flow, Colin can easily see its overall logic and also sees the cryptic coded data. When he finds the 200-page PDF manual that contains the lookup tables (LUTs) for the cryptic codes, the process looks daunting. An example lookup table in the PDF is shown in
Colin starts with some of the more important tables. He finds that he can select the table in the PDF file and paste it into flow pane 313. In some cases, the data in the table is not entirely correct, but it does a reasonable job, and Colin can then manually patch up the results in the data pane 315, saving him considerable time. As he works, he sees his results immediately. If the tables don't align, he sees so right away.
Ultimately, Colin brings in a dozen LUTs that seem particularly relevant to the analysis his team performs, and publishes the results so his team can use the data. As people ask for more information about specific columns, Colin can further augment his flow to bring in additional LUTs.
Danielle, a developer at a major software company, is looking at data that represents build times. Danielle has a lot of control over the format of the data, and has produced it in a nice consumable CSV format, but wants to simply load it and append it to a database she's created.
As she loads up the data, she scans the profile view 314. Something immediately looks odd to her: there are a few builds with negative times. There's clearly something wrong here, and she wants to debug the problem, but she also wants to pull the data together for analysis.
She selects the negative times in the profile view, and clicks “Keep only” to retain only the erroneous rows. She adds a target to flow these into a file. She's going to use those raw rows to guide her debugging.
Going back to her flow, she adds another branch right before the filter. She again selects the negative values (e.g., in the profile pane 314 or the data pane 315), and then simply presses “delete.” This replaces the values with null, which is a good indicator that the real value simply isn't known. She proceeds with the rest of her simple flow, appending the build data to her database, and she will look into the negative values later.
Earl works for a car manufacturer, and is responsible for maintaining a dataset that shows the current status of each vehicle and major part in the factory. The data is reported to a few operational stores, but these operational stores are quite large. There are hundreds of thousands of parts, and as an automated facility, many thousands of records are mechanically created for each vehicle or part as it proceeds through the factory. These operational stores also contain many records that have nothing to do with part status, but other operational information (e.g., “the pressure in valve 134 is 500 kPa”). There is a business need for a fast, concise record for each part.
Earl drags the tables for each of the three relational operational stores into the flow pane 313. Two of them store data as single tables containing log records. The third has a small star schema that Earl quickly flattens by dragging and dropping to create a join.
Next, through additional dragging and dropping, Earl is able to quickly perform a jagged union of the tables. In the result, he can drag-and-drop columns together and the interface coalesces the results for him.
The part identification number is a little problematic: one system has a hyphen in the value. Earl takes one of the values in the data pane 315, selects the hyphen, and presses delete. The interface infers a rule to remove the hyphens from this column, and inserts a rule into the flow that removes the hyphen for all of the data in that column.
Earl doesn't want most of the status codes because they are not relevant to his current project. He just wants the status codes that relate to parts. He pulls in a table that has information on status codes and drops it on the last node of his flow, resulting in a new join on the status code. He now selects only those rows with “target type” equal to “part” and chooses “Keep only” to filter out the other values. This filtering is done in the profile pane 314 or the data pane 315.
Finally, Earl only wants the last value for each part. Through a direct gesture, he orders the data in the data pane by date, groups by part number, and adds a “top-n” table calculation to take only the final update for each part.
Earl runs his flow, and finds that it takes four hours to run. But he knows how he can speed this up. He can record the last time he ran his flow, and only incorporate new records on each subsequent run. To do this, however, he needs to update existing rows in his accumulated set, and only add rows if they represent new parts. He needs a “merge” operation.
Earl uses the part number to identify matches, and supplies actions for when a match occurs or does not occur. With the update logic, Earl's flow only takes 15 minutes to run. The savings in time lets the company keep much tighter track of where parts are in their warehouse and what their status is.
Earl then pushes this job to a server so it can be scheduled and run centrally. He could also create a scheduled task on his desktop machine that runs the flow using a command-line interface.
Gaston works at at an investment broker in a team responsible for taking data produced by IT and digesting it so that it can be used by various teams that work with customers. IT produces various data sets that show part of a customer's portfolio—bond positions, equity positions, etc. —but each alone is not what Gaston's consumers need.
One team, led by Hermine, needs all of the customer position data pulled together so that her team can answer questions their customers have when they call in. The data preparation is not that complex.
Gaston does some massaging of the nightly database drops IT produces, unions them together, and does some simple checks to make sure the data looks okay. He then filters it down to just what Hermine's team needs and creates a TDE for her team to use.
With their previous tools, Gaston had to remember to come in and run the flow every morning. But with the new data prep application 250, this flow can be treated declaratively. He sends a TDS to Hermine that her team uses, so every data visualization that Hermine's team makes runs directly against the database. This means Gaston doesn't have to worry about refreshing the data, and it executes quickly.
Another team, led by Ian, uses similar data to do performance reviews of his customers' accounts. To produce this data, Gaston reuses the work he's done for Hermine, but filters the data to Ian's team's accounts, and then performs an additional flow to join the data with various indexes and performance indicators so that Ian's team can perform their analysis. This work is expensive and doesn't seem to perform well live. If he runs the flow, it takes several hours to complete—but Ian's team only needs this once a month. Gaston sets up a recurring calendar item on the server to run it once each month.
Karl is a strategic account manager for a major software company. He is trying to use Tableau to visualize information about attendees at an industry conference, who they work for, who their representatives are, whether they are active or prospective customers, whether their companies are small or large, and so on.
Karl has a list of the conference attendees, but he's been down this road before. The last time he was in this position, it took him 8 hours to clean up the list—and 15 minutes to build the visualization once he was done. This time he's using the data preparation application 250 to speed up and automate the process.
Karl first wants to clean up the company names. Eyeballing the data, he sees what he'd expect: the same company is often listed in multiple different formats and some of them are misspelled. He invokes a fuzzy deduplication routine provided on the operation palette to identify potential duplicates. He reviews the results and corrects a couple cases where the algorithm was over-eager. He also finds a few cases that the algorithm missed, so he groups them. This yields a customer list with canonical company names.
He then tries to join his data with a list of companies kept in a data source on his Tableau Server. He finds that each company has multiple listings. Multiple different companies may have the same name, and a single company may have multiple accounts based on region.
To sort this out, Karl uses a REST connector for LinkedIn™ that he's found, and passes it each of the email addresses in his data to retrieve the country and state for each person. This procedure takes the information he has (e.g., the person's name, the person's company, and the person's position) and uses LinkedIn's search functionality to come up with the best result for each entry. He then joins the company and location data to the data in his Server to find the correct account.
Karl finds that his join doesn't always work. The canonical company name he picked doesn't always match what is in his accounts database. He converts his join to a fuzzy join, reviews the fuzzy matches, and further corrects the result manually.
Now that he has his data cleaned up, he opens it up in Tableau to create his data visualization.
Commonly used features of flows include:
Data preparation applications are sometimes classified as ETL (extract, transform, and load) systems. Each of the three phases performs different types of tasks.
In the extract phase, users pull data from one or more available data sources. Commonly, users perform these tasks:
In the transform phase, users transform the data in a wide variety of ways. Commonly, the transformations include these tasks:
In the load phase, a user stores the results so that the results can be analyzed. This includes:
Once a user has constructed a flow to prepare data, the user often needs to:
Disclosed systems 250 give control to users. In many cases, the data prep application makes intelligent choices for the user, but the user is always able to assert control. Control often has two different facets: control over the logical ordering of operations, which is used to ensure the results are correct and match the user's desired semantics; and physical control, which is mostly used to ensure performance.
Disclosed data prep application 250 also provide freedom. Users can assemble and reassemble their data production components however they wish in order to achieve the shape of data they need.
Disclosed data prep applications 250 provide incremental interaction and immediate feedback. When a user takes actions, the system provides feedback through immediate results on samples of the user's data, as well as through visual feedback.
Typically, ETL tools use imperative semantics. That is, a user specifies the details of every operation and the order in which to perform the operations. This gives the user complete control. In contrast, an SQL database engine evaluates declarative queries and is able to select an optimal execution plan based on the data requested by the query.
Disclosed implementations support both imperative and declarative operations, and a user can select between these two execution options at various levels of granularity. For example, a user may want to exercise complete control of a flow at the outset while learning about a new dataset. Later, when the user is comfortable with the results, the user may relinquish all or part of the control to the data prep application in order to optimize execution speed. In some implementations, a user can specify a default behavior for each flow (imperative or declarative) and override the default behavior on individual nodes.
Disclosed implementations can write data to many different target databases, including a TDE, SQL Server, Oracle, Redshift, flat files, and so on. In some instances, a flow creates a new data set in the target system. In other instances, the flow modifies an existing dataset by appending new rows, updating existing rows, upserting rows, or deleting rows.
Errors can occur while running a flow. Errors can include transient system issues, potential known error condition in the data, for which the user may encode corrective action, and implicit constraints that the author did not consider. Disclosed implementations generally handle these error conditions automatically when possible. For example, if the same error condition was encountered in the past, some implementations reapply a known solution.
Although a flow is essentially a data transformation, implementations enable users to annotate their outputs with declarative modelling information to explain how the outputs can be used, viewed, validated, or combined. Examples include:
Disclosed implementations generally include these components:
Some data visualization applications are able to execute data prep flows and can use TDEs or other created datasets to construct data visualizations.
Disclosed implementations can also import some data flows created by other applications (e.g., created in an ETL tool).
Implementations enable users to:
With access to a configured Server, a user can:
The output of a node can be directed to more than one following node. There are two basic cases here. In the first case, the flows diverge and do not come back together. When the flows do not converge, there are multiple outputs from the flow. In this case, each branch is effectively a separate query that consists of all predecessors in the tree. When possible, implementations optimize this so that the shared portion of the flow is not executed more than once.
In the second case, the flow does converge. Semantically, this means that the rows flow though both paths. Again, the flow execution generally does not double execute the ancestors. Note that a single flow can have both of these cases.
The user interface:
Users can add filters to a flow of arbitrary complexity. For example, a user can click to add a filter at a point in the flow, and then enter a calculation that acts as a predicate. In some implementations, the calculation expressions are limited to scalar functions. However, some implementations enable more complex expressions, such as aggregations, table calculations, or Level of Detail expressions.
A user can edit any filter, even if it was inferred by the system. In particular, all filters are represented as expressions.
The profile pane 314 and data pane 315 provide easy ways to create filters. For example, some implementations enable a user to select one or more data values for a column in the data pane, then right-click and choose “keep only” or “exclude.” This inserts a filter into the flow at the currently selected node. The system infers an expression to implement the filter, and the expression is saved. If the user needs to modify the filter later, it is easy to do so, regardless of whether the later time is right away or a year later.
In the profile pane 314, a user can select a bucket that specifies a range of values for a data field. For example, with a categorical field, the range is typically specified as a list of values. For a numeric field, the range is typically specified as a contiguous range with an upper or lower bound. A user can select a bucket and easily create a filter that selects (or excludes) all rows whose value for the field fall within the range.
When a user creates a filter based on multiple values in one column or multiple buckets for one column, the filter expression uses OR. That is, a row matches the expression if it matches any one of the values or ranges.
A user can also create a filter based on multiple data values in a single row in the data pane. In this case, the filter expression uses AND. That is, only rows that match all of the specified values match the expression. This can be applied to buckets in the profile pane as well. In this case, a row much match on each of the selected bucket ranges.
Some implementations also allow creation of a filter based on a plurality of data values that include two or more rows and include two or more columns. In this case, the expression created is in disjunctive normal form, with each disjunct corresponding to one of the rows with a selected data value. Some implementations apply the same technique to range selections in the profile window as well.
Note that in each of these cases, a user visually selects the data values or buckets, then with a simple gesture (e.g., right-click plus a menu selection) creates a filter that limits the rows to just the selected values or excludes the selected values. The user does does have to figure out how to write an expression in correct Boolean logic.
As illustrated above with respect to
Some implementations provide simplified or condensed versions of flows as nodes and annotations. In some implementations, a user can toggle between a full view or a condensed view, or toggle individual nodes to hide or expose the details within the node. For example, a single node may include a dozen operations to perform cleanup on certain source files. After several iterations of experimentation with the cleanup steps, they are working fine, and the user does not generally want to see the detail. The detail is still there, but the user is able to hide the clutter by viewing just the condensed version of the node.
In some implementations, operational nodes that do not fan out are folded together into annotations on the node. Operations such as joins and splits will break the flow with additional nodes. In some implementations, the layout for the condensed view is automatic. In some implementations, a user can rearrange the nodes in the condensed view.
Both the profile pane and the data pane provide useful information about the set of rows associated with the currently selected node in the flow pane. For example, the profile pane shows the cardinalities for various data values in the data (e.g., a histogram showing how many rows have each data value). The distributions of values are shown for multiple data fields. Because of the amount of data shown in the profile pane, retrieval of the data is usually performed asynchronously.
In some implementations, a user can click on a data value in the profile pane and see proportional brushing of other items. When a user selects a specific data value, the user interface:
In some implementations, rows are not displayed in the data pane unless specifically requested by the user. In some implementations, the data pane is always automatically populated, with the process proceeding asynchronously. Some implementations apply different standards based on the cardinality of the rows for the selected node. For example, some implementations display the rows when the cardinality is below a threshold and either does not display the rows or proceeds asynchronously if the cardinality is above the threshold. Some implementations specify two thresholds, designating a set of rows as small, large, or very large. In some implementations, the interface displays the rows for small cardinalities, proceeds to display rows asynchronously for large cardinalities, and does not display the results for very large cardinalities. Of course the data pane can only display a small number of rows, which is usually selected by sampling (e.g., every nth row). In some implementations, the data pane implements an infinite scroll to accommodate an unknown amount of data.
Disclosed data prep applications provide a document model that the User Interface natively reads, modifies, and operates with. This model describes flows to users, while providing a formalism for the UI. The model can be translated to Tableau models that use AQL and the Federated Evaluator to run. The model also enables reliable caching and re-use of intermediate results.
As illustrated in
A Loom doc 702 is the model that describes the flow that a user sees and interacts with directly. A Loom doc 702 contain all the information that is needed to perform all of the ETL operations and type checking. Typically, the Loom doc 702 does not include information that is required purely for rendering or editing the flow. A Loom doc 702 is constructed as a flow. Each operation has:
There are four major types of operations: input operations, transform operations, output operations, and container operations.
The input operations perform the “Extract” part of ETL. They bind the flow to a data source, and are configured to pull data from that source and expose that data to the flow. Input operations include loading a CSV file or connecting to an SQL database. A node for an input operation typically has zero inputs and at least one output.
The transform operations perform the “Transform” part of ETL. They provide “functional” operations over streams of data and transform it. Examples of transform operations include “Create Calculation as ‘[HospitalName]−[Year]’”, “Filter rows that have hospitalid=‘HarbourView’”, and so on. Transform nodes have at least one input and at least one output.
The output operations provide the “Load” part of ETL. They operate with the side effects of actually updating the downstream data sources with the data stream that come in. These nodes have one input, and no output (there are no “outputs” to subsequent nodes in the flow).
The container operations group other operations into logical groups. These are used to help make flows easier to document. Container operations are exposed to the user as “Nodes” in the flow pane. Each container node contains other flow elements (e.g., a sequence of regular nodes), as well as fields for documentation. Container nodes can have any number of inputs and any number of outputs.
A data stream represents the actual rows of data that moves across the flow from one node to another. Logically, these can be viewed as rows, but operationally a data stream can be implemented in any number of ways. For example, some flows are simply compiled down to AQL (Abstract Query Language).
The extensible operations are operations that the data prep application does not directly know how to evaluate, so it calls a third-party process or code. These are operations that do not run as part of the federated evaluator.
The logical model 704 is a model that contains all of the entities, fields, relationships, and constraints. It is built up by running over the flow, and defines the model that is built up at any part in the flow. The fields in the logical model are column in the results. The entities in the logical model represent tables in the results, although some entities are composed of other entities. For example, a union has an entity that is a result of other entities. The constraints in the logical model represents additional constraints, such as filters. The relationships in the logical model represent the relationships across entities, providing enough information to combine them.
The physical model 706 is the third sub-model. The physical model includes metadata for caching, including information that identifies whether a flow needs to be re-run, as well as how to directly query the results database for a flow. The metadata includes:
This data is used for optimizing flows as well as enabling faster navigation of the results.
The physical model includes a reference to the logical model used to create this physical model (e.g. a pointer to a file or a data store). The physical model 706 also includes a Tableau Data Source (TDS), which identifies the data source that will be used to evaluate the model. Typically, this is generated from the logical model 704
The physical model also includes an AQL (Abstract Query Language) query that will be used to extract data from the specified data source.
As illustrated in
The file format has a multi-document format. In some implementations, the file format has three major parts, as illustrated in
The file format 710 includes a Loom Doc 702, as described above with respect to
The file format 710 also includes local data 714, which contains any tables or local data needed to run a flow. This data can be created through user interactions, such as pasting an HTML table into the data prep application, or when a flow uses a local CSV file that needs to get uploaded to a server for evaluation.
The Evaluation Sub-System is illustrated in
There are two basic contexts for evaluating a flow, as illustrated in
In navigation and analysis (730), a user is investigating a dataset. This can include looking at data distributions, looking for dirty data, and so on. In these scenarios, the evaluator generally avoids running the entire flow, and instead runs faster queries directly against the temporary databases created from running the previous the flows previously.
These processes take advantage of good metadata around caching in order to make sure that smart caching decisions are possible.
Some implementations include an Async sub-system, as illustrated in
In some implementations, the async model includes four main components:
Implementation of cancel operations depend on where the cancellation occurs. In the browser layer, it is easy to send a cancel request, and then stop polling for results. In the REST API, it is easy to send a cancel event to a thread that is running.
Some implementations make it safe and easy to “re-factor” a flow after it is already created. Currently, ETL tools allow people to make flows that initially appear fairly simple, but become impossible to change as they get bigger. This is because it is hard for people to understand how their changes will affect the flow and because it is hard to break out chunks of behavior into pieces that relate to the business requirements. Much of this is caused by the user interface, but the underlying language needs to provide the information needed by the UI.
Disclosed implementations enable users to create flows that can be easily refactored. What this means is that users are able to take operations or nodes and easily:
In some implementations, when a user is refactoring a flow, the system helps by identifying drop targets. For example, is a user selects a node and begins to drag it within the flow pane, some implementations display locations (e.g., by highlighting) where the node can be moved.
Disclosed data prep applications use a language that has three aspects:
These languages are distinct, but layer on top of each other. The expression language is used by the flow language, which in turn can be used by the control flow language.
The language describes a flow of operations that logically goes from left to right, as illustrated in
The data flow language is the language most people associate with the data prep application because it describes the flow and relationship that directly affect the ETL. This part of the language has two major components: models and nodes/operations. This is different from standard ETL tools. Instead of a flow directly operating on data (e.g. flowing actual rows from a “filter” operation to an “add field” operation) disclosed flows define a logical model that specifies what it wants to create and the physical model defining how it wants to materialize the logical model. This abstraction provides more leeway in terms of optimization.
Models are the basic nouns. They describe the schema and the relationships of the data that is being operated on. As noted above, there is a logical model and a separate physical model. A Logical Model provides the basic “type” for a flow at a given point. It describes the fields, entities, and relationships that describe the data being transformed. This model includes things such as sets and groups. The logical model specifies what is desired, but not any materialization. The core parts of this model are:
A flow can include one or more forks in the logical model. Forking a flow uses the same Logical Model for each fork. However, there are new entities under the covers for each side of the fork. These entities basically pass through to the original entities, unless a column gets projected or removed on them.
One reason to create new entities is to keep track of any relationships across entities. These relationships will continue to be valid when none of the fields change. However, if a field is modified it will be a new field on the new entity so the relationship will be known not to work anymore.
Some implementations allow pinning a node or operation. The flows describe the logical ordering for a set of operations, but the system is free to optimize the processing by making the physical ordering different. However, a user may want to make sure the logical and physical orderings are exactly the same. In these cases, a user can “pin” a node. When a node is pinned, the system ensures that the operations before the pin happen physically before the operations after the pin. In some cases, this will result in some form of materialization. However, the system streams through this whenever possible.
The physical model describes a materialization of the logical model at a particular point. Each physical model has a reference back to the logical model that was used to generate it. Physical models are important to caching, incremental flow runs, and load operations. A physical model includes a reference to any file that contains results of a flow, which is a unique hash describing the logical model up to this point. The physical model also specifies the TDS (Tableau Data Source) and the AQL (Abstract Query Language) generated for a run.
Nodes and Operations are the basic verbs. Nodes in the model include operations that define how the data is shaped, calculated, and filtered. In order to stay consistent with the UI language, the term “operations” refers to one of the “nodes” in a flow that does something. Nodes are used to refer to containers that contain operations, and map to what a user sees in the flow pane in the UI. Each specialized node/operation has properties associated with it that describe how it will operate.
There are four basic types of nodes: input operations, transform operations, output operations, and container nodes. Input operations create a logical model from some external source. Examples include an operation that imports a CSV. Input operations represent the E in ETL (Extract). Transform operations transform a logical model into a new logical model. A transform operation takes in a logical model and returns a new logical model. Transform nodes represent the T in ETL (Transform). An example is a project operation that adds a column to an existing logical model. Output operations take in a logical model and materialize it into some other data store. For example, an operation that takes a logical model and materializes its results into a TDE. These operations represent the L in ETL (Load). Container nodes are the base abstraction around how composition is done across flows, and also provide an abstraction for what should be shown as the nodes are shown in the UI.
As illustrated in
Type checking is performed in two phases. In the type environments creation phase, the system runs through the flow in the direction of the flow. The system figures out what types are needed by each node, and what type environments they output. If the flow is abstract (e.g., it does not actually connect to any input nodes), the empty type environment is used. Type refinement is the second phase. In this phase, the system takes the type environments from the first phase and flow them “backwards” to see if any of the type narrowing that happened in type environment creation created type conflicts. In this phase, the system also creates a set of required fields for the entire sub flow.
Each operation has a type environment associated with it. This environment contains all the fields that are accessible and their types. As illustrated in
An environment can be either “Open” or “Closed”. When an environment is Open, it assumes that there may be fields that it does not know about. In this case, any field that is not known will be assumed to be any type. These fields will be added to the AssumedTypes field. When an environment is Closed, it assumes it knows all the fields, so any fields that is not knows is a failure.
All known types are in the Types member. This is a mapping from field names to their types. The type may be either another type environment or it can be a Field. A field is the most basic type.
Each field is composed of two parts. basicTypes is a set of types that describes the possible set of types for the field. If this set has only one element, then we know what type it has. If the set is empty, then there was a type error. If the set has more than one element, then there are several possible types. The system can resolve and do further type narrowing if needed. derivedFrom is a reference to the fields that went into deriving this one.
Each field in a scope has a potential set of types. Each type can be any combination of Boolean, String, Integer, Decimal, Date, DateTime, Double, Geometry, and Duration. There is also an “Any” type, which is shorthand for a type that can be anything.
In the case of open Type Environments, there may be cases of fields that are known to not exist. For example, after a “removeField” operation, the system may not know all the fields in the Type Environment (because it is open), but the system does know that the field just removed is not there. The type Environment property “NotPresent” is used to identify such fields.
The AssumedTypes property is a list of the types that were added because they were referenced rather than defined. For example, if there is an expression [A]+[B] that is evaluated in an open type environment, the system assumes that there were two fields: A and B. The AssumedTypes property allows the system to keep track of what was added this way. These fields can be rolled up for further type winnowing as well as for being able to determine the required fields for a container.
The “Previous” type environment property is a reference to the type environment this one was derived from. It is used for the type refinement stages, during the backwards traversal through the flow looking for type inconsistencies.
Type environments can also be composed. This happens in operations that take multiple inputs. When, a type environment is merged, it will map each type environment to a value in its types collection. Further type resolution is then delegated to the individual type environments. It will then be up to the operator to transform this type environment to the output type environment, often by “flattening” the type environment in some way to create a new type environment that only has fields as types.
This is used by Join and Union operators in order to precisely use all of the fields from the different environments in their own expressions, and having a way to map the environment to an output type environment.
The type environment created by an input node is the schema returned by the data source it is reading. For an SQL database, this will be the schema of the table, query, stored procedure, or view that it is extracting. For a CSV file, this will be the schema that is pulled from the file, with whatever types a user has associated with the columns. Each column and its type is turned into a field/type mapping. In addition, the type environment is marked as closed.
The type environment for a transform node is the environment for its input. If it has multiple inputs, they will be merged to create the type environment for the operation. The output is a single type environment based on the operator. The table in
A container node may have multiple inputs, so its type environment will be a composite type environment that routes appropriate children type environments to the appropriate output nodes. When a container is pulled out to be re-used, it resolves with empty type environments for each input to determine its dependencies.
In some implementations, a container node is the only type of node that is able to have more than one output. In this case, it may have multiple output type environments. This should not be confused with branching the output, which can happen with any node. However, in the case of branching an output, each of the output edges has the same type environment.
There are a few cases where type errors are flagged when the system discovers conflicting requirements for a field. Unresolved fields are not treated as errors at this stage because this stage can occur over flows with unbounded inputs. However, if a user tried to run a flow, unresolved variables would be a problem that is reported.
Many of inputs have specific definitions of types. For example, specific definitions include using CHAR(10) instead of VARCHAR(2000), what collation a field uses, or what scale and precision a Decimal type has. Some implementations do not track these as part of the type system, but do track them as part of the runtime information.
The UI and middle tier are able to get at the runtime types. This information is able to flow through the regular callback, as well as being embedded in the types for tempdb (e.g., in case the system is populating from a cached run). The UI shows users the more specific known types, but does not type check based on them. This enables creation of OutputNodes that use more specific types, while allowing the rest of the system to use the more simplified types.
Most of the type errors are found in the type checking stage. This comes right after calculating the initial type environments, and refines the scopes based on what is known about each type.
This phase starts with all the terminal type environments. For each type environment, the system walks back to its previous environments. The process walks back until it reaches a closed environment or an environment with no previous environment. The process then checks the types in each environment to determine if any fields differ in type. If so and the intersection of them is null, the process raises a type error. If any of the fields differ in type and the intersection is not null, the process sets the type to be the intersection and any affected nodes that have their type environments recalculated. In addition, any types that are “assumed” are added to the previous type environment and the type environments is recalculated.
There are a few subtleties that are tracked. First, field names by themselves are not necessarily unique, because a user can overwrite a field with something that has a different type. As a result, the process uses the pointer from a type back to the types used to generate it, thereby avoiding being fooled by unrelated things that resolve to the same name at different parts in the graph. For example, suppose a field A has type [int, decimal], but then there is a node that does a project that makes A into a string. It would be an error to go back to earlier versions of A and say the type doesn't work. Instead, the backtracking at this point will not backtrack A past the addField operation.
Type checking narrows one variable at a time. In the steps above, type checking is applied to only one variable, before re-computing the known variables. This is to be safe in the case there is an overloaded function with multiple signatures, such as Function1(string, int) and Function1(int, string). Suppose this is called as Function1([A], [B]). The process determines that the types are A:[String, int] and B: [String,int]. However, it would be invalid for the types to resolve to A:[String] and B:[String], because if A is a String, B needs to be an int. Some implementations handle this type of dependency by re-running the type environment calculation after each type narrowing.
Some implementations optimize what work to do by only doing work on nodes that actually have a required field that includes the narrowed variable. There is a slight subtlety here, in that narrowing A may end up causing B to get narrowed as well. Take the Function1 example above. In these cases, the system needs to know when B has changed and check its narrowing as well.
When looking at how operators will act, it is best to think of them in terms of four major properties, identified here as “Is Open”, “Multi-Input”, “Input Type”, and “Resulting Type”.
An operation is designated as Open when it flows the columns through. For example, “filter” is an open operation, because any column that are in the input will also be in the output. Group by is not Open, because any column that is not aggregated or grouped on will not be in the resulting type.
The “Multi-Input” property specifies whether this operation takes multiple input entities. For example, a join is multi-input because it takes two entities and makes them one. A union is another operation that is multi-input.
The “Input Type” property specifies the type the node requires. For a multi-input operation, this is a composite type where each input contains its own type.
The “Resulting Type” property specifies the output type that results from this operation.
The tables in
In many instances, a flow is created over time as needs change. When a flow grows by organic evolution, it can become large and complex. Sometimes a user needs to modify a flow, either to address a changing need, or to reorganize a flow so that it is easier to understand. Such refactoring of a flow is difficult or impossible in many ETL tools.
Implementations here not only enable refactoring, but assist the user in doing do. At a technical level, the system can get the RequireFields for any node (or sequence of nodes), and then light up drop targets at any point that has a type environment that can accommodate it.
Another scenario involves reusing existing nodes in a flow. For example, suppose a user wants to take a string of operations and make a custom node. The custom node operates to “normalize insurance codes”. The user can create a container node with a number of operations in it. The system can then calculate the required fields for it. The user can save the node for future use, either using a save command or dragging the container node to the left-hand pane 312. Now, when a person selects the node from the palette in the left-hand pane, the system lights up drop targets in the flow, and the user can drop the node onto one of the drop targets (e.g., just like the refactoring example above).
ETL can get messy, so implementations here enable various system extensions. Extensions include
Implementations here take an approach that is language agnostic in terms of how people use the provided extensibility.
A first extension allows users to build custom nodes that fit into a flow. There are two parts to creating an extension node:
Some implementations define two node types that allow for user-defined extensions. A “ScriptNode” is a node where the user can write script to manipulate rows, and pass them back. The system provides API functions. The user can then write a transform (or input or output) node as a script (e.g., in Python or Javascript). A “ShellNode” is a node where the user can define an executable program to run, and pipe the rows into the executable. The executable program will then write out the results to stdout, write errors to stderr and exit when it is done.
When users create extensions for flows, the internal processing is more complex. Instead of compiling everything down to one AQL statement, the process splits the evaluation into two pieces around the custom node, and directs the results from the first piece into the node. This is illustrated in
In addition to customizations that modify the flowing data in some way, users can write scripts that control how a flow runs. For example, suppose a user needs to pull data from a share that has spreadsheets published to it each day. A defined flow already knows how to deal with CSV or Excel files. A user can write a control script that iterates over a remote share, pulls down the relevant files, then runs the over those files.
There are many common operations, such as pattern union, that a user can add into a data flow node. However, as technology continues to evolve, there will always be ways to get or store data that are not accommodated by the system-defined data flow nodes. These are the cases where control flow scripts are applicable. These scripts are run as part of the flow.
As noted above, flows can also be invoked from the command line. This will allow folks to embed the scripts in other processes or overnight jobs.
Implementations have a flow evaluation process that provides many useful features. These features include:
The evaluation process works based on the interplay between the logical models and physical models. Any materialized physical model can be the starting point of a flow. However, the language runtime provides the abstractions to define what subsections of the flows to run. In general, the runtime does not determine when to run sub-flows versus full flows. That is determined by other components.
Although physical models can be reordered to optimize processing, the logical models hide these details from the user because they are generally not relevant. The flow evaluator makes it look like the nodes are evaluated in the order they are shown in the flow. If a node is pinned, it will actually cause the flow to materialize there, guaranteeing that the piece on the left evaluates before the one on the right. In a forked flow, the common pre-flow is run only once. The process is idempotent, meaning that input operators can be called again due to a failure and not fail. Note that there is no requirement that the data that comes back is exactly the same as it would have been the first time (i.e., when the data in the upstream datasource has changed between the first and second attempts).
Execution of transform operators has no side-effects. On the other hand, extract operators typically do have side-effects. Any operation that modifies the data sources before it in the flow are not seen until the next run of the flow. Load operators generally do not have side effects, but there are exceptions. In fact, some load operators require side-effects. For example, pulling files down from a share and unzipping them are considered side effects.
Some implementations are case sensitive with respect to column names, but some implementations are not. Some implementations provide a user configurable parameter to specify whether column names are case sensitive.
In general, views of cached objects always go “forward” in time.
As noted above, a user can edit data values directly in the data grid 315. In some instances, the system infers a general rule based on the user's edit. For example, a user may add the string “19” to the data value “75” to create “1975.” Based on the data and the user edit, the system may infer a rule that the user wants to fill out the character string to form 4 character years for the two character years that are missing the century. In some instances, the inference is based solely on the change itself (e.g., prepend “19”), but in other instances, the system also bases the inference on the data in column (e.g., that the column has values in the range “74”-“99”). In some implementations, the user is prompted to confirm the rule before applying the rule to other data values in the column. In some implementations, the user can also choose to apply the same rule to other columns.
User edits to a data value can include adding to a current data value as just described, removing a portion of a character string, replacing a certain substring with another substring, or any combination of these. For example, telephone numbers may be specified in a variety of formats, such as (XXX)YYY-ZZZZ. A user may edit one specific data value to remove the parentheses and the dash and add dots to create XXX.YYY.ZZZZ. The system can infer the rule based on a single instance of editing a data value and apply the rule to the entire column.
As another example, numeric fields can have rules inferred as well. For example, if a user replaces a negative value with zero, the system may infer that all negative values should be zeroed out.
In some implementations, a rule is inferred when two or more data values are edited in a single column of the data grid 315 according to a shared rule
The terminology used in the description of the invention herein is for the purpose of describing particular implementations only and is not intended to be limiting of the invention. As used in the description of the invention and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof.
The foregoing description, for purpose of explanation, has been described with reference to specific implementations. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The implementations were chosen and described in order to best explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to best utilize the invention and various implementations with various modifications as are suited to the particular use contemplated.
This application is a continuation of U.S. patent application Ser. No. 15/345,391, filed Nov. 7, 2016, titled “User Interface to Prepare and Curate Data for Subsequent Analysis,” which is incorporated by reference herein in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| Parent | 15345391 | Nov 2016 | US |
| Child | 18518420 | US |
