![]() ![]() A numerical survey of the optical response of the various structures, as well as the effect of size and aspect ratio, reveals their rich array of resonances, which are supported by single-particle optical scattering experiments. These are strikingly different from what is obtained for typical plasmonic metals because Mg crystallizes in a hexagonal close packed structure, as opposed to the cubic Al, Cu, Ag, and Au. ![]() Here, we report numerical predictions and experimental verifications of a set of shapes based on Mg NPs displaying various twinning patterns including (101̅1), (101̅2), (101̅3), and (112̅1), that create tent-, chair-, taco-, and kite-shaped NPs, respectively. LSPR properties also depend on composition traditional, rare, and expensive noble metals (Ag, Au) are increasingly eclipsed by earth-abundant alternatives, with Mg being an exciting candidate capable of sustaining resonances across the ultraviolet, visible, and near-infrared spectral ranges. Their resonant frequency is dictated by the nanoparticle (NP) shape and size, fueling much research geared toward discovery and control of new structures. When using the Kite API, you can add properties using of some metals can sustain light-driven electron oscillations called localized surface plasmon resonances, or LSPRs, that give rise to absorption, scattering, and local electric field enhancement. When using the CLI create or update commands, you add custom properties with the option -set prop.name=value. In addition to setting existing system properties, you can create your own key-value pairs to use as custom properties in your application. When working with Crunch, you can use CrunchDatasets.partition methods to restructure data so that all of the records stored in a given partition are processed by the same writer. ![]() The recommended way to avoid out of memory exceptions is to write to fewer files. It is important that this number doesn’t exceed a reasonable portion of the heap memory allocated to the process, or else the write could fail with an OutOfMemoryException. That means that the upper bound for a writer’s memory consumption is multiplied by the -size. The amount of data kept in memory for each file could be up to the Parquet block size in bytes. For example, Parquet defines, which is approximately the amount of data that is buffered before writing a group of records (a row group). Kite passes descriptor properties to the underlying file formats. kite-dataset update dataset:hdfs:/user/me/datasets/annual_earnings -set -size=12 To increase the writer cache size, you can use the CLI update command. If you increase the writer cache to 12, files for all 12 months are open at once. ![]() The application must constantly close and open files, which slows down your writes. There are 12 partitions, but, by default, only 10 files are open at the same time. For example, you might have an application writing a year’s worth of data to year/month partitions. If the number of open files exceeds the cache size, Kite closes the file that was used least recently.įor some applications, adjusting the writer cache size can improve performance. When the writer receives a record that goes in a new partition (one for which there isn’t an open file) it creates a new file in that partition. Writers open one file per partition to which they write records. size controls the number of files kept open by an HDFS or Hive dataset writer. You can set properties on your datasets with when using the API, or with the -set option from the Kite CLI. These settings are especially relevant for Parquet, because it buffers records in memory. There are several dataset properties that affect how files are written. You also have the option of creating custom properties for additional control over how Kite reads or writes your data. Kite gives you the option of changing settings of existing system properties to enhance performance. ![]()
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