Some of you have asked me previously whether or not we can share any test documents to demonstrate Calc’s new OpenCL-based formula engine. Thanks to AMD, we can now make available 3 test documents that showcase the performance of the new engine, and how it compares to Calc’s existing engine as well as Excel’s.
These files are intentionally in Excel format so that they can be used both in Calc and Excel. They also contain VBA script to automate the execution of formula cell recalculation and measure the recalculation time with a single button click.
All you have to do is to open one of these files, click “Recalculate” and wait for it to finish. It should give you the number that represents the duration of the recalculation in milliseconds.
Note that the 64-bit version of Excel requires different VBA syntax for calling native function in DLL, which is why we have a separate set of documents just for that version. You should not use these documents unless you want to test them specifically in the 64-bit version of Excel. Use the other one for all the rest.
On Linux, you need to use a reasonably recent build from the master branch in order for the VBA macro to be able to call the native DLL function. If you decide to run them on Linux, make sure your build is recent enough to contain this commit.
Once again, huge thanks to AMD for allowing us to share these documents with everyone!
Just a quick update to my last post on getting Calc’s border line situation sorted out.
As of last post, the border lines were pretty in good shape as far as printing to paper, but it was still less than satisfactory when rendered on screen. Lines looked generally fatter and the dashes line were unevenly positioned. I had some ideas that I wanted to try out in order to make the border lines look prettier on screen. So I went ahead and spent a few extra days to give that a try, and I’m happy to report that that effort paid off.
To recap, this is what the border lines looked like as of last Friday.
and this is what they look like now:
The lines are skinnier, which in my opinion make them look slicker, and the dashes lines are now evenly spaced and look much better.
I spent this past week on investigating a collection of various problems surrounding how Calc draws cell borders. The problem is very hard to define and can become very subjective depending on who you talk to. Having said that, if you ever imported an Excel document that makes elaborate use of cell borders into Calc, you may often have seen that the borders were printed somewhat differently than what you would have expected.
When you open this document in Calc and print it, you probably get something like this:
You’ll immediately notice that some of the lines (hair, dashed and double lines to be precise) are not printed at all! Not only that, thin, medium and thick lines are a little skinner than those of Excel’s, the dotted line is barely visible, the medium dashed line looks a lot different, and the rest of the dashed lines all became solid lines.
Therefore, it was time for action.
I’ll spare you the details, but the good news is that after spending a week in various parts of the code base, I’ve been able to fix most of the major issues. Here is what Calc prints now using the build from the latest master branch:
There are still some minor discrepancies from Excel’s borders, such as the double line being a bit too thinner, the dotted line being not as dense as Excel’s etc. But I consider this a step in the right direction. The dashed and medium dashed lines look much better to my eye, and the thicknesses of these lines are more comparable to Excel’s.
The dash-dot and dash-dot-dot lines still become solid lines since we don’t yet support those line types, but that can be worked on at a later time.
So, this is all good, right?
Not quite. One of the reasons why the cell borders became such a big issue was that we previously focused too much on getting them to display correctly on screen. Unfortunately, the resolution of a typical PC monitor is not high enough to accurately depict the content of your document, so what you see on screen is a pixelized approximation of the actual content. When printing to a paper, on the other hand, the content gets depicted much more accurately simply because you get much higher resolution when printing.
I’ll give you a side-by-side comparison of how the content of the same document gets displayed in Excel (2010), Calc 4.2 (before my change), and Calc master (with my change) all at 100% zoom level.
First up is Excel:
The lines all look correct, unsurprisingly. One thing to note is that when displaying Excel approximates a hairline with a very thin, densely dotted line to differentiate it from a thin line both of which are one pixel high. But make no mistake; hairline by definition is a solid line. This is just a trick Excel employs in order to make the hairline look thinner than the thin line counterpart.
Then comes Calc as of 4.2 (before my change):
The hairline became a finely-dashed line both on display and in internal representation. Aside from that, both dashed and medium dashed lines look a bit too far apart. Also, the double line looks very much single. In terms of the line thicknesses, however, they do look very much comparable to Excel’s. Let me also remind you that Excel’s dash-dot and dash-dot-dot lines currently become solid lines in Calc because we don’t support these line types yet.
Now here is what Calc displays after my change:
The hair line is a solid line since we don’t use the same hair line trick that Excel uses. The dotted and dashes lines look much denser and in my opinion look better. The double line is now really double. The line thicknesses, however, are a bit off even though they are internally more comparable to Excel’s (as you saw in the printout above). This is due to the loss of precision during rasterization of the border lines, and for some reason they get fatter. We previosly tried to “fix” this by making the lines thinner internally, but that was a wrong approach since that also made the lines thinner even when printed, which was not a good thing. So, for now, this is a compromise we’ll have to live with.
But is there really nothing we can do about this? Well, we could try to apply some correction to make the lines look thinner on screen, and on screen only. I have some ideas how we may be able to achieve that, and I might give that a try during my next visit.
That, and we should also support those missing dash-dot, and dash-dot-dot line types at some point.
Here is another performance improvement that just landed on master.
It was brought to our attention that the performance of saving documents to ODF spreadsheet format had been degrading quite noticeably. This was especially true when the document contained lots of what we call rich text cells. Rich text cells are those cells that contain text with mixed format spans, or text that consists of multiple lines. These cells are handled differently from simple strings internally, and have slightly more overhead than the simple string counterparts. Because of this, saving a document full of such texts was always slower than saving one with just numbers and simple strings.
However, even with this unavoidable overhead, the performance of saving rich text cells was clearly going in the wrong direction. Therefore it was time to act.
Long story short, after many days of code reading and writing, I brought it to a state where I can share some numbers.
Measuring export performance
I measured the performance of exporting rich text cells in the following steps.
Create a new spreadsheet document.
Type in cell A1 3 lines of ‘libreoffice’. Here, you can hit Ctrl-Enter to move to the next line within the same cell.
Copy A1, select A1:N1000 and paste, to replicate the content of A1 to all cells in the range.
Save the document as ODF spreadsheet document, and measure its duration.
I performed the above measurement with 3.5, 3.6, 4.0, 4.1, and the latest master (slated to become 4.2) builds, and these are the numbers.
It is clear from this chart that the performance started to suffer first in version 3.6, then gradually worsened over 4.0 and 4.1. The good news is that we have managed to bring the number back down in the master build, even lower than that of 3.5 which I used as the point of reference. Not just slightly lower, but much, much lower.
I don’t know about you, but I’m quite happy with this result.
This week I have finally finished implementing a true shared formula framework in Calc core which allows Calc to share token array instances between adjacent formula cells if they contain identical set of formula tokens. Since one of the major benefits of sharing formula token arrays is reduced memory footprint, I decided to measure the trend in Calc’s memory usages since 4.0 all the way up to the latest master, to see how much impact this shared formula work has made in Calc’s overall memory footprint.
Here is the test document I used to measure Calc’s memory usage
This ODF spreadsheet document contains 100000 rows of cells in 4 columns of which 399999 are formula cells. Column A contains a series of integers that grow linearly down the column. Here, only the first cell (A1) is a numeric cell while the rest are all formula cells that reference their respective immediate upper cell. Cells in Column B all reference their immediate left in Column A, cells in Column C all reference their immediate left in Column B, and so on. References used in this document are all relative references; no absolute references are used.
I’ve tested a total of 4 builds. One is the 4.0.1 build packaged for openSUSE 11.4 (x64) from the openSUSE repository, one is the 4.0.6 build built from the 4.0 branch, one is the 4.1.1 build built from the 4.1 branch, and the last one is the latest from the master branch. With the exception of the packaged 4.0.1 build, all builds are built locally on my machine running openSUSE 11.4 (x64). Also on the master build, I’ve tested memory usage both with and without shared formulas.
In each tested build, the memory usage was measured by directly opening the test document from the command line and recording the virtual memory usage in GNOME system monitor. After the document was loaded, I allowed for the virtual memory reading to stabilize by waiting several seconds before recording the number. The results are presented graphically in the following chart.
The following table shows the actual numbers recorded.
4.0.1 (packaged by openSUSE)
master (no shared formula)
master (shared formula)
Additionally, I’ve also measured the number of token array instances between the two master builds (one with shared formula and one without), and the build without shared formula created 399999 token array instances (exactly 4 x 100000 – 1) upon file load, whereas the build with shared formula created only 4 token array instances. This likely accounts for the difference of 78.3 MiB in virtual memory usage between the two builds.
Effect of cell storage rework
One thing worth noting here is that, even without shared formulas, the numbers clearly show a steady decline of Calc’s memory usage from 4.0 to 4.1, and to the current master. While we can’t clearly infer from these numbers alone what caused the memory usage to shrink, I can say with reasonable confidence that the cell storage rework we did during the same period is a significant factor in such memory footprint shrinkage. I won’t go into the details of the cell storage rework here; I’ll reserve that topic for another blog post.
Oh by the way, I have absolutely no idea why the 4.0.1 build packaged from the openSUSE repository shows such high memory usage. To me this looks more like an anomaly, indicative of earlier memory leaks we had later fixed, different custom allocator that only the distro packaged version uses that favors large up-front memory allocation, or anything else I haven’t thought of. Either way, I’m not counting this as something that resulted from any of our improvements we did in Calc core.
Last week was SUSE’s Hack Week – an event my employer does periodically to allow us – hard working engineers – to go wild with our wildest ideas and execute them in one week. Just like what I did at my last Hack Week event, I decided to work on integration of Orcus library into LibreOffice once again, to pick up on what I’d left off from my previous integration work.
Prior to Hack Week, orcus was already partially integrated; it was used to provide the backend functionality for Calc’s XML Source feature, and experimental support for Gnumeric file import. The XML Source side was pretty well integrated, but the normal file import side was only partially integrated. Lots of essential pieces were still missing, the largest of which were
support for multiple filters from a single external filter provider source (such as orcus),
progress indicator in the status bar, and
proper type detection by analyzing file content rather than its extension (which we call “deep detection”).
In short, I was able to complete the first two pieces during Hack Week, while the last item still has yet to be worked on. Aside from this, there are still more minor pieces missing, but perhaps I can work on the remaining bits during the next Hack Week.
Enabling orcus in your build
If you have a recent enough build from the master branch of the LibreOffice repository, you can enable imports via orcus library by
checking the Enable experimental features box in the Options dialog, and
setting the environment variable LIBO_USE_ORCUS to YES before launching Calc.
This will overwrite the stock import filters for ODS, XLSX and CSV. At present, orcus only performs file extension based detection rather than content based one, so be mindful of this when you try this on your machine. To go back to the current import filters, simply disable experimental features, or unset the environment variable.
Note that I’ve added this bits to showcase a preview of what orcus can potentially do as a future import filter framework. As such, never use this in production if you want stable file loading experience, or don’t file bugs against this. We are not ready for that yet. Orcus filters are still missing lots and lots of features.
This is perhaps the most interesting part. I wanted to do a quick performance comparison and see how this orcus filter stands up against the current filter. Given the orcus filter is still only capable of importing raw cell values and not any other features or properties (not even cell formats), I’ve used this test file which only consists of raw text and numeric values in a 8-by-300000 range, to measure the load times that are as fair and representative as I could make them. Here is the result on my machine running openSUSE 11.4:
The current filter, which has undergone its set of performance optimizations on raw cell values, still spends upwards of 50 seconds. Given that it used to take minutes to load this file, it’s still an improvement.
The orcus filter, on the other hand, combined with the heavily optimized load handler in Calc core that I put in place during Hack Week, can load the same file in 4.5 seconds. I would say that is pretty impressive.
I also measured the load time on the same file using Excel 2007, on the same machine running on top of wine, and the result was 7.5 seconds. While running an Windows app via wine emulation layer may incur some performance cost, this page suggests that it should not be noticeable, if any. And my own experience of running various versions of Excel via wine backs up that argument. So this number should be fairly representative of Excel’s native performance on the same hardware.
Considering that my ultimate goal with orcus is to beat Excel on performance on loading its own files (or at least not be slower than Excel), I would say we are making good progress toward that goal.
That’s all for today. Thank you, ladies and gentlemen.
Last week was SUSE Hack Week, where we SUSE engineers were encouraged to be creative and work on whatever project that we had been dying to work on.
Given this opportunity, I decided to try integrating my orcus library project into LibreOffice proper to see how much improvement we could make in the performance of loading spreadsheet documents.
I’ll leave the detailed description and goal of orcus project for another blog post, but in short, orcus is an independent library designed to process spreadsheet documents, and is also designed to be useable from an application that would like to use it to load documents. It’s currently still work in progress, and is not even in alpha quality. So, I intentionally don’t release orcus library packages on an official basis.
The main difficulty with integrating orcus into LibreOffice proper was dealing with the very intricate loading process that LibreOffice uses for all existing filters. It first goes through an elaborate type detection process, which loads the content of the file into memory in order for the type detection code to parse it. Once the correct type is determined, LibreOffice then instantiates correct frame loader and start the actual loading process. I’ve explained all of this in detail in this blog post of mine.
Orcus, on the other hand, only needs a file path, and it does the rest. And it pushes data to the call back functions provided by the client code as it parses the file. It was this difference in overall loading process that made the integration of orcus into LibreOffice all the more challenging. And even though the hack week itself lasted only one week, I had spent months prior to it just to study the type detection code and other auxiliary code that makes up the whole file loading process in order to come up with an elegant way to add hook for orcus.
Long story short, I was able to come up with a way to hook orcus such that LibreOffice relinquishes all its file loading to the orcus library, and only handles callbacks. To make this work, I first packaged orcus into an installable rpm package using the openSUSE build service, locally installed that package, then added –with-system-orcus configure option to allow LibreOffice to find the library. The entire change needed to add hook is condensed into this commit.
Using CSV filter as an experiment
As an initial experiment, I replaced the current csv import filter with one from orcus, just to see how this overall process works. The results are very encouraging.
With a very large csv file I created via this python script:
#!/usr/bin/env pythonimportsysfor i inxrange(0,65536):
for j inxrange(1,101):
val = i * 1.0 / j
for i in xrange(0, 65536):
for j in xrange(1, 101):
val = i * 1.0 / j
the current filter spends roughly 27 seconds to load this file, which is not too bad given the sheer size of the file (~50Mb). The orcus filter, on the other hand, spends only 11 seconds to load the same file.
However, the orcus filter code path still skips a number of steps that need to be performed if it were to be used in the production build, such as
drawing progress bar in the status bar area,
calculating row heights for rows that include multi-line cell contents, and
probably something else I forget to mention here.
Given some of these can be quite expensive, the above numbers may not be fully comparable. Despite that, these initial numbers show a great promise on the performance improvement that may result from using the orcus library.
First of all, we will not switch to the orcus csv filter anytime soon. Although I’d like to see that happen at some point in the future, there are still lots of missing pieces in the orcus csv filter that prevent us from using it in the production build. My plan with orcus is therefore limited to addition of new filters, and my immediate plan is to develop new XML import and export filters using orcus, and integrate it into LibreOffice. This should also provide a stepping stone for any additional filters that may come up later, as well as replacing some of the existing filters as the need arises.
As my previous post just mentioned, mdds 0.6.0 is finally released which contains two new data structures: multi_type_vector and multi_type_matrix. I’d like to explain a little more about multi_type_vector in this post because, of all the data structures I’ve added to mdds over the course of its project life, I firmly believe this structure deserves some explanation.
What motivated multi_type_vector
The initial idea for this structure came from a discussion I had with Michael Meeks over two years ago in Nuremberg, Germany. Back then, he was dumping his idea on me about how to optimize cell storage in LibreOffice Calc, and his idea was that, instead of storing cell values wrapped around cell objects allocated on the heap and storing them in a column array, we store raw cell values directly in an array without the cell object wrappers. This way, if you have a column filled with numbers from top down, those values are guaranteed to be placed in a contiguous region in memory space which is more likely to be in the same memory page unless their size exceeds the memory page size. By contrast, if you store cell values wrapped inside cell objects that are allocated on the heap, those values are most likely scattered all around the memory space and probably located in many different memory pages.
Now, one of the most common operations that typical spreadsheet users do is to operate on numbers in cells. It could be summing up their totals, calculating their average, determining their minimum and maximum values and so on and so forth. To make these operations happen, the program first needs to fetch all the cell values before it can work on them.
Assume that these values are stored inside cell objects which are located in hundreds of memory pages. The mere action of fetching the cell values alone requires loading all of these memory pages, which causes the CPU to fetch them from the main memory in order to access them. Worse, if some of those pages are located in instead of the physical memory space but in the virtual memory space, it causes page fault, which further degrades performance since that particular memory page must be swapped in from disk. In contrast, if they are all located in a single memory page (or just several of them instead of hundreds), it just needs to fetch just once or several times, depending on the size of the data being fetched.
Moreover, most CPUs these days come equipped with CPU caches to cache recently-fetched memory pages in order to speed up subsequent access to them. Because of this, keeping all your data in the same page reduces the chance of the CPU fetching it from the main memory (or the worse case from the virtual memory), which is slower than fetching it from the caches.
Let’s visualize this idea for a moment. The current cell storage looks like this:
As you can see, cells are scattered in different pages. To access them all, you need to load all of these pages that contain the requested cell objects.
Compare that with the following illustration:
where all requested cell values are stored in a single array that’s located in a single page. I hope it’s obvious by now which one actually fetches data faster.
Calc currently employs the former storage model, and our hope is to make Calc’s storage model more efficient both space- and time-wise by switching to the latter model.
Applying this to the design
One difficulty with applying this concept to column storage is that, a column in a typical spreadsheet application allows you to store values of different types. Cells containing a bunch of test scores may have in the same column a title cell at the top that stores the text “Score”. Likewise, those test scores may be followed by an empty cell followed by a bunch of formula cells containing formula expressions summing, averaging, or counting the test scores. Since one array can only hold values of identical type, this requires us to use a separate array for each segment of identical cell type.
With that, the column storage structure becomes somewhat like this:
An empty cell segment doesn’t store any value array, but it does store its size which is necessary to calculate the logical position of the next non-empty element.
This is the basic design of the multi_type_vector structure. It stores values of each identical type in a single, secondary value array while the primary column array stores the memory locations of all secondary value arrays. It’s important to point out that, while I used the spreadsheet use case as an example to explain the basic idea of the structure, the structure itself can be used in other, much broader use cases, and is not specific to spreadsheet applications.
In the next section, I will talk about challenges I have faced while implementing this structure. But first one terminology note: from now on I will use the term “element block” (or simply “block”) to refer to what was referred to as “secondary value array” up to this point. I use this name in my implementation code too, so using this name makes easier for me to explain things.
The basic design of multi_type_vector is not that complicated and was not very challenging to understand and implement. What was more challenging was to handle cases where a value, or a series of values, are inserted over a block or blocks of different types. There are a variety of ways to insert new values into this container, and sometimes the new values overlap the existing blocks of different types, or overlap a part of an existing block of the same type and a part of a block of a different type, and so on and so forth. Because the basic design of the container requires that the type of every element block differs from its neighbors’, some data insertions may cause the container to need to re-organize its element block structure. This posed quite a challenge since multi_type_vector supports the following methods of modifications:
set a single value to overwrite an existing one if any (set() method, 2-parameter variant),
set a sequence of values to overwrite existing values if any (set() method, 3-parameter variant),
insert a sequence of values and shift those existing values that occur below the insertion position (insert() method),
set a segment of existing values empty (set_empty() method), and
insert a sequence of empty values and shift those existing value that occur below the insertion position (insert_empty() method),
and each of these scenarios requires different strategy for element block re-organization. Non-overwriting data insertion scenarios (insert() and insert_empty()) were somewhat easier to handle than the overwriting data insertion scenarios (set() and set_empty()), as the latter required more branching and significantly more code to cover all cases.
This challenge was further exacerbated by additional requirement to support a “managed” element block that stores pointers to objects whose life cycle is managed by the block. I decided to add this one for convenience reasons, to allow transitioning the current cell storage model into the new storage model in several phases rather than doing it in one big-bang change. During the transition phase, we will likely convert the number and string cells into raw value element blocks, while keeping more complex cell structures such as formula cells still wrapped in their current form. This means that, during the transition we will have element blocks storing pointers to heap-allocated formula cell objects scattered across memory space. Eventually these formula objects need to be stored in a contiguous memory space but that will have to wait after the transition phase.
Supported data types
Template containers are supposed to work with any custom types, and multi_type_vector is no exception. But unlike most standard template containers which normally have one primary data type (and perhaps another one for associative containers), multi_type_vector allows storage of unspecified numbers of data types.
By default, multi_type_vector supports the following data types: bool, short, unsigned short, int, unsigned int, long, unsigned long, double, and std::string. If these data types are all you need when using multi_type_vector, then you won’t have to do anything extra, and just instantiate the template instance by
mtv_type data(10);// set initial size to 10.// insert values.
mtv_type data(10); // set initial size to 10.
// insert values.
But if you need to store other types of data, you’ll need to do a little more work. Let’s say you have this class type:
and you want to store instances of this class in multi_type_vector. I’ll skip the actual definition of this class, but let’s assume that the basic stuff such as default and copy constructors, equality operator etc are all implemented and working properly.
First, you need to define a unique numeric ID for your custom type. Each element type must be associated with a numeric ID. The IDs for standard data types are defined as follows:
The value of element_type_user_start defines the starting number of all custom type IDs. IDs for the standard types all come before this value. If you only want to define one custom type ID, then just using that value will be sufficient. If you need another ID, just add 1 to it and use it for that type. As long as each ID is unique, it doesn’t really matter what their actual values are.
Next, you need to choose the block type. There are 3 block types to choose from:
The last 2 are relevant only when you need a managing pointer element block to store heap objects. Right now, let’s just use the default element block for your custom type.
Note that these callbacks functions are called from within multi_type_vector via unqualified call, so it’s essential that they are in the same namespace as the custom data type in order to satisfy C++’s argument-dependent lookup rule.
So far so good. The last step that you need to do is to define a structure of element block functions. This is also a boiler plate, and for a single custom type case, you can define something like this:
This is quite a bit of code, I know. I should definitely work on making it a bit simpler to use with a lot less typing in future versions of mdds. Anyway, with this in place, we can finally define the multi_type_vector type:
With all these bits in place, you can finally start using this container:
data.set(0, foo);// Insert a custom data element.
data.set(1, 12.3);// You can still use the standard data types.
data.set(0, foo); // Insert a custom data element.
data.set(1, 12.3); // You can still use the standard data types.
That’s all I will talk about custom data types for now. I hope this gives you a glimpse of how this container works in general.
Since this is the very first incarnation of multi_type_vector, I have no doubt this still has a lot of issues to be worked out. One immediate issue that comes to mind is the performance of element position lookup. Given a logical position of the element, the container first has to locate the right element block that stores the specified element, but this lookup always happens from the first element block. So, if you are doing a continuous lookup of million’s of elements in a loop, the overall lookup speed can be quite slow since each lookup starts from the first block. Speeding up this operation is certainly a task to be worked on in the near future. Meanwhile, the user of this container can resort to using the iterators to iterate through the element blocks and their member elements.
Another issue is the verbosity of the element block function structure required for custom element blocks. This can be worked out by providing templatized structures per number of custom data types. This one is probably easier to solve, and I should look into that soon.
I have great news to share with you. Calc’s ODS import filter in 3.5 should be substantially faster when you have documents with a large number of named ranges. Read on if you want to know more details.
Laurent Godard, Markus Mohrhard, and myself have been working pretty hard in the past month to bring the performance of ODS import filter to a reasonable level, especially with documents containing a large number of named ranges.
Here is the background. Laurent uses LibreOffice as a platform for his professional extension, which makes heavy use of named ranges. It programmatically generates ODS documents and inserts hundred’s or thousand’s of named ranges as intermediary storage to further process the data. The problem was, however, our import performance with that kind of documents was so suboptimal that this process was taking a prohibitively long time. In order for his extension to perform optimally, our ODS import filter needed to be optimized, and optimized heavily.
During the Paris conference, we got our heads together in order to come up with a strategy to make that happen. Laurent was more than willing to participate this effort, and in the end, he did substantial amount of work profiling, analyzing code, coming up with optimization strategy and putting it altogether. Markus and I provided mentorship, code pointers, as well as occasional coding to accelerate this effort.
Our hope was to make it all happen in time for our first 3.5 release. And I’m very happy to say that we made it.
Since we are talking about performance, it won’t be complete without the actual numbers. So here goes.
Test document 1
Here is the first test document global500.ods. It contains 500 sheets, 12,500 global named ranges, and 12,500 formulas that reference them.
On my development machine, the last stable release 3.4.4 takes 14 seconds to open this document. While 14 seconds may not seem that slow, keep in mind that this machine is somewhat unfairly fast tailored for the abusive developer use, so the real world performance is likely much less impressive (you can probably multiply that number by 3 to get a rough idea of the real world performance). Anyhow, using the latest master branch on the same machine, this document opens roughly in 2 and a half seconds. That’s roughly 86% reduction in import time.
Test document 2
Here is the second, somewhat larger document global1000.ods. This document contains 1000 sheets, 25,000 named ranges and 25,000 formulas that reference them.
According to my benchmark performed in the same condition as the first document, 3.4.4 opens this document in 50 seconds, whereas in 3.5.0 it opens under 5 seconds. That’s about 90% reduction in import time. Pretty impressive!
Real power of open source
This story shows another aspect of this remarkable achievement worth mentioning. If you use an open source product such as LibreOffice in your business, and if it doesn’t perform the way you need it to, you can actually join the project as a developer and coordinate the effort with the upstream developers to make it happen. And depending on the nature of the change you want to see happen, it can happen very quickly as this story demonstrates.
I wanted to emphasize this because, while more and more businesses and institutions are embracing open source software, many of them tend to focus too much on the cost-saving aspect of it, thereby developing the wrong mindset that that’s what open source is all about. It isn’t. The real power of using open source software in your deployment is it gives you the ability to join and contribute to the project to influence the direction of its development. That gives you real flexibility in planning, and in my opinion the best way to harness the power of using open source software. The monetary cost-saving side of the benefit comes as a side effect but should be thought of only as an added bonus, not the primary reason for deploying open source software.
I’m happy to announce that I’ve managed to squeeze this new feature in just in time for the 3.5 code freeze.
As I’ve mentioned briefly in G+, I’ve been working on brushing up the age-old autofilter popup window in the past few weeks. I have no idea how old the old one is, but it’s been there for as long as I remember. In case anyone needs a reminder as to what the old one looks like, here it is.
It’s functional, yet very basic. While this has served us for many years since the last century, it was also clear that the world has since moved on, and the people has started craving for modern looks and eye candies even in the office productivity applications. Clearly, it was time for a change.
In contrast to the old, here is how the new one looks:
I don’t know about you, but I really like the new one better. :-)
Aside from updating the aged look of the old popup, I was also motivated to introduce the new popup for its ability to allow selection of multiple values from the selection list.
You may think that this new popup looks somewhat familiar. That’s because the same popup is also used as the pivot table (formerly data pilot) field member selection popup. I’ve touched on this previously on my blog, and you’ll probably notice the similarity when comparing the screenshot of the new popup with the screenshot of the pivot table popup included in that post.
Internally these two use the same code. In fact, when I developed that feature for the pivot table, I intentionally designed it to be re-usable, precisely so that I could use it for the autofilter popup at a later time.
So, the hard part of implementing the new popup had already been finished. All I had to do was to put the autofilter functionality into the popup and launch it instead of the ugly old one, which is precisely what I did to bring the new popup into reality. I also had to refactor the code that performs the filtering to allow multi-value matching, which was, while invisible to the users, not a trivial task.
The work is not totally done yet. As of this writing, the xlsx filter has not been fully adopted to take advantage of the new multi-selection capability, but that’s my next task, and I expect that to be done in time for 3.5.
Also, the menu still looks very basic, and contains only the same set of options that the old popup had. This was done deliberately in order for us to ship it in time for 3.5, by avoiding the rather expensive process of re-designing the menu part of the popup. But I expect we work on the re-design post-3.5, to make it even better and more usable. Note that the new popup is fully capable of doing sub menus, which gives us all sorts of possibilities.
Anyhow, that’s all I have to say about this at the moment. I hope you guys will enjoy the new and shiny autofilter popup! :-)
Notes for testing
As with any new features, this one needs lots of testing. I’ve written new unit test to cover some parts of it, but unit test can’t cover all corners of use cases (especially those involving UI interactions), and manual testing from real users is always appreciated. Some of the affected areas I can think of are:
Built-in functions MATCH, LOOKUP, HLOOKUP and VLOOKUP that use the core filtering code which I’ve heavily refactored.
Import and export of the existing filtering rules, with ods, xls, and xlsx.
Filtering with pivot tables, which shares parts of the filtering code that has been refactored.
Standard and advanced filter dialogs
So, watch out for the next daily build that includes this feature!