1.DataSpread : Unifying Databases and Spreadsheets Mangesh Bendre
2.Data Analytics to the People Most of the people doing ad-hoc data manipulation and analysis use spreadsheets, e.g., Excel Why? Easy to use : direct manipulation of data Built-in visualization capabilities Flexible : no need for a schema
3.But Spreadsheets are Terrible! Slow s ingle change wait minutes on a 10,000 x 10 spreadsheet can’t even open a spreadsheet with >1M cells speed by itself can prevent analysis Tedious + not Powerful filters via copy-paste only FK joins via VLOOKUPs; others impossible even simple operations are cumbersome B rittle sharing excel sheets around, no collab /recovery using spreadsheets for collaboration is painful and error-prone 3
4.Let’s turn to Databases Databases are: Slow Scalable Tedious + not Powerful Powerful and expressive (SQL) Brittle Collaboration, recovery, succinct So why not use databases? Well, for the same reason why spreadsheets are so useful: Easy to use Not easy to use Built-in visualization No built-in visualization Flexible Not flexible 4
5.Combining the benefits of spreadsheets and databases Spreadsheet as a frontend interface Databases as a backend engine Result: retain the benefits of both! But it’s not that simple…
6.Different Ideologies Databases and spreadsheets have different ideologies that need to be reconciled… Due to this, the integration is not trivial… Feature Databases Spreadsheets Data Model Schema-first Dynamic/No Schema Addressing Tuples with PK Cells, using Row/Col Presentation Set-oriented, no such notion Notion of current window, order Modifications Must correspond to queries Can be done at any granularity Computation Query at a time Value at a time 6
7.High Level Idea Key Thrusts Scalability Millions to billions of rows Expressiveness Express complex operations easily Capabilities Collaboration, versioning
8.Current Progress Version 0.1: I ntegration of spreadsheets and databases. Scalable spreadsheets. Version 0.3: Support for tables on a spreadsheet Relational Operators. SQL Asynchronous formula computation. Multi-cell functions. Capture dependency using database.
9.Initial Prototype: Excel Based
10.Current Prototype: Web Based
11.Scalability Goal : To effortlessly handle spreadsheets with billions of cells. Problem: How do we represent the data to allow efficient access ? 11
12.Problem : Representation Q: how do we represent spreadsheet data? Dense spreadsheets: represent as tables (Row #, Col1 val , Col2 val , …) Sparse spreadsheets: represent as triples (Row #, Column #, Value) 12
13.Problem : Representation However, even if we only use “tables”, carving out the ideal # partitions (min. storage, modif ., access) is NP-Hard Reduction from min. edge-length partition of rectilinear polygons Thankfully, we have a way out … 13
14.Solution: Constrain the Problem A new class of partitionings : recursive decomp . A very natural class of partitionings ! 14
15.Solution: Constrain the Problem The optimal recursive decomp . partitioning can be found in PTIME using DP Still quadratic in # rows, columns Merge rows/columns with identical signatures ~ the time for a single scan 15
16.Problem : Spreadsheet operations Q: how do we support spreadsheet operations like insert/delete row/column? Inserting a row requires updating the row numbers of subsequent rows. 16
17.Solution : Positional Indexing A : Indexing structure that enables efficient spreadsheet operations. Insert, Delete and Lookup in O(log N) 17
19.Current Prototype PostgreSQL backend ZK spreadsheet open-source web frontend Comfortably scales to arbitrarily many rows + additional features. Hopefully bring spreadsheets to the big data age! 19 1224560
20.Problem: Navigation 20 How to browse big data ?
21.Solution: Navigation Support 21
22.Scalability Goal : To effortlessly handle spreadsheets with billions of cells and formulae . Problem: How do we maintain interactivity while dealing with large and complex formulae ? 22
23.Interactivity for Formula Computation Observation: A formula that accesses a million cell cannot be executed interactively (<500ms). Solution: Asynchronous formula computation. Challenges: How to present a consistent view of the spreadsheet? 23
24.Asynchronous formula Computation Consistent view:- Never show incorrect data to users. Identify dependent cells in a constant time. Tolerate False positives. Dependency Graph Compression . Computation Scheduling I/O is a bottleneck. How to fetch cells for efficient computation ? 24
25.Expressivity Goal : To make spreadsheets more expressive. Inspired from relational databases. S upport the notion of tables on spreadsheets Relational algebra. SQL. 25
26.Table Management Tables synced with the backend in real-time . Create table Link Unlink Add tuples Delete tuples Add Rows Delete Rows.
27.Relational Operators. UNION DIFFERENCE INTERSECTION CROSSPRODUCT SELECT PROJECT RENAME JOIN Input is a table Output is a table cell.
28.Multi Cell Functions.
29.Support for SQL. Leverage backend Database Input is a SQL Output is a table cell.