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Ingrid Abriola
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The faster warming rate in the Arctic compared to the globe as a whole is nowadays considered a robust fact. The phenomenon, called Arctic or polar amplification (AA), can be seen in both instrumental observations1,2,3 and climate models4 as well as in paleoclimate proxy records5.
During the last decade, multiple factors have been proposed to explain the potential causes of AA: enhanced oceanic heating and ice-albedo feedback due diminishing sea ice6,7,8,9, Planck feedback10, lapse-rate feedback11, near-surface air temperature inversion12, cloud feedback13, ocean heat transport14 and meridional atmospheric moisture transport15,16,17. Furthermore, the reduced air pollution in Europe may have contributed to the Arctic warming during the last decades18,19, and possible reductions of Asian aerosols under a strong mitigation policy may increase the future AA20. In climate models, it has been shown21 that AA occurs rapidly in response to external forcings due to atmospheric lapse rate feedback, with sea ice-related feedbacks becoming more important later on. A recent study22 reported a stronger future AA in a low than a high-emission scenario due to the faster melting of sea ice and weaker ice-albedo feedback.
When the temperature trends shown in Fig. 1b are divided by the multi-dataset global mean temperature trend at each grid-point, we get the spatial map of 43-year local Arctic amplification (AA43), or simply local amplification when calculated for areas south of the Arctic circle (Fig. 1c). Values higher than one indicate that those regions are warming faster than the global average, while values below one correspondingly indicate a slower warming. The AA43 maps for individual observational datasets are provided in the Supplementary Fig. S3.
By considering the seasonality of AA (Fig. 5), we see that AA is the strongest in the late autumn (November) and the weakest in the warm season (July). This is consistent in both CMIP6 models and the observations, and in line with the earlier study conducted with ERA-Interim reanalysis data and CMIP5 models8. Thus, over the past 43 years, the October-December months in the Arctic have warmed five times faster than the globe, while the warming ratio is close to two in June-August (Fig. 5). The stronger AA in late autumn arises from the newly opened water areas that act to enhance upwelling longwave radiation and turbulent fluxes of sensible and latent heat from the sea into the atmosphere8.
As the Earth moved out of ice ages over the past million years, the global temperature rose a total of 4 to 7 degrees Celsius over about 5,000 years. In the past century alone, the temperature has climbed 0.7 degrees Celsius, roughly ten times faster than the average rate of ice-age-recovery warming.
Models predict that Earth will warm between 2 and 6 degrees Celsius in the next century. When global warming has happened at various times in the past two million years, it has taken the planet about 5,000 years to warm 5 degrees. The predicted rate of warming for the next century is at least 20 times faster. This rate of change is extremely unusual.
The results were clear no matter the language. For English, speech recognition was three times faster than typing, and the error rate was 20.4 percent lower. In Mandarin Chinese, speech was 2.8 times faster, with an error rate 63.4 percent lower than typing.
Observations from 11 satellite missions monitoring the Greenland and Antarctic ice sheets have revealed that the regions are losing ice six times faster than they were in the 1990s. If the current melting trend continues, the regions will be on track to match the "worst-case" scenario of the Intergovernmental Panel on Climate Change (IPCC) of an extra 6.7 inches (17 centimeters) of sea level rise by 2100.
So let your take-away be this: read/write latency forSQLite is competitive with read/write latency of individual files ondisk. Often SQLite is faster. Sometimes SQLite is almostas fast. Either way, this article disproves the commonassumption that a relational database must be slower than directfilesystem I/O.
Jim Grayand others studied the read performance of BLOBsversus file I/O for Microsoft SQL Server and found that reading BLOBs out of the database was faster for BLOB sizes less than between 250KiB and 1MiB.(Paper).In that study, the database still stores the filename of the content evenif the content is held in a separate file. So the database is consultedfor every BLOB, even if it is only to extract the filename. In thisarticle, the key for the BLOB is the filename, so no preliminary databaseaccess is required. Because the database is never used at all whenreading content from individual files in this article, the thresholdat which direct file I/O becomes faster is smaller than it is in Gray'spaper.
Depending on your hardware and operating system, you should see that reads from the test1.db database file are about 35% faster than reads from individual files in the test1.dir or test1.tree folders. Results can varysignificantly from one run to the next due to caching, so it is advisableto run tests multiple times and take an average or a worst case or a bestcase, depending on your requirements.
The --blob-api option on the database read test causes kvtest to usethe sqlite3_blob_read() feature of SQLite to load the content of theblobs, rather than running pure SQL statements. This helps SQLite to runa little faster on read tests. You can omit that option to compare theperformance of SQLite running SQL statements.In that case, the SQLite still out-performs direct reads, thoughby not as much as when using sqlite3_blob_read().The --blob-api option is ignored for tests that read from individual diskfiles.
The chart below shows average time to read a blob directly from thefilesystem versus the time needed to read the same blob from the SQLite database.The actual timings vary considerably from one system to another (the Ubuntu desktop is muchfaster than the Galaxy S3 phone, for example). This chart shows the ratio of thetimes needed to read blobs from a file divided by the time needed tofrom the database. The left-most column in the chart is the normalizedtime to read from the database, for reference.
The chart shows that on Windows10, content can be read from the SQLitedatabase about 5 times faster than it can be read directly from disk.On Android, SQLite is only about 35% faster than reading from disk.
Further performance improves can be made by using thememory-mapped I/O feature of SQLite. In the next chart, theentire 1GB database file is memory mapped and blobs are read(in random order) using the sqlite3_blob_read() interface.With these optimizations, SQLite is twice as fast as Androidor MacOS-X and over 10 times faster than Windows.
The -DSQLITE_DIRECT_OVERFLOW_READ compile-time option causes SQLiteto bypass its page cache when reading content from overflow pages. Thishelps database reads of 10K blobs run a little faster, but not all that muchfaster. SQLite still holds a speed advantage over direct filesystem readswithout the SQLITE_DIRECT_OVERFLOW_READ compile-time option.
Other compile-time options such as using -O3 instead of -Os orusing -DSQLITE_THREADSAFE=0 and/or some of the otherrecommended compile-time options might help SQLite to run even fasterrelative to direct filesystem reads.
The size of the blobs in the test data affects performance.The filesystem will generally be faster for larger blobs, sincethe overhead of open() and close() is amortized over more bytes of I/O,whereas the database will be more efficient in both speed and spaceas the average blob size decreases.
SQLite is much faster than direct writes to disk on Windowswhen anti-virus protection is turned on. Since anti-virus softwareis and should be on by default in Windows, that means that SQLiteis generally much faster than direct disk writes on Windows.
Some other SQL database engines advise developers to store blobs in separatefiles and then store the filename in the database. In that case, wherethe database must first be consulted to find the filename before openingand reading the file, simply storing the entire blob in the databasegives much faster read and write performance with SQLite.See the Internal Versus External BLOBs article for more information.
*If you have already registered for a June or July camp you are eligible to receive a 25% discount on any 2021 camp that you register for through the rest of the year! If you are already registered for a June or July camp please email pa...@fitterandfaster.com before 9am EST on Friday, June 18th to receive your 25% off code. The code you receive will be valid for 24-hours once you receive it.
This is absolutely correct, although even serial processing may beat 26 minutes. Although Tom was doing the project for fun, often people use Hadoop and other so-called Big Data (tm) tools for real-world processing and analysis jobs that can be done faster with simpler tools and different techniques.
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