![]() ![]() This will find all records on the matching days that have a daily change greater then. ![]() This will find records that have same yearMonthDay_s field value returnedīy the initial search, and will return records for all tickers on those days.Ī filter query is applied to the search to filter the search to rows that have a change_d In this case the yearMonthDay_s is mapped back to the yearMonthDay_s field in the same index. The walk parameter maps a field from the search results to a field in the index. The nodes function wraps the search function and operates over its results. This search returns all fields in the index including the yearMonthDay_s which is the string representation of the year, month, and day of the matching records. Where the ticker symbol is jpm and the change_d field (daily change in stock price) is greater then. The inner search expression finds records between a specific date range The example below finds stock tickers whose daily movements tend to be correlated with the ticker jpm (JP Morgan). In this example the focus will be on finding correlated nodes in a time series This function is covered in detail in the section Graph Traversal. The nodes function performs aggregations of nodes during a breadth first search of a graph. Its not clear that these terms appear more frequently in Brooklyn then This returns the five most common complaint types in Brooklyn, but In the first example the facet function aggregates the top 5 complaint types The example below illustrates the difference between the facet function and the significantTerms function. The significantTerms function can often provide insights that cannot be gleaned from other types of aggregations. The foreground and background counts are global for the collection. The background count is how many documents the term appears in in the entire corpus. The foreground count is how many documents the term appears in in the result set. The significantTerms function emits a tuple for each term which contains the term, the score, the foreground count and the background count. This function scores terms based on how frequently they appear in the result set and how rarely they appear in the entire corpus. The significantTerms function queries a collection, but instead of returning documents, it returns significant terms found in documents in the result set. The timeseries function supports any combination of the following aggregate functions: count(*), sum, avg, min, max. Of the plot so they can be more easily studied.īy studying the scatter plot we can learn a number of things about the The effect of this is to spread the filesize_d samples across the length Plotted on the y-axis and the x sequence plotted on the x-axis. The visualization below shows a scatter plot with the filesize_d field This sequence is returned in a field called x. When called without the field list parameter ( fl) the random function also generates a sequence, 0-499 in this case, which can be used for plotting the x-axis. When called with no other parameters the random function returns a random sample of 500 records with all fields from the collection. In the example below the random function is called in its simplest form with just a collection name as the parameter. Is covered in the Statistics, Probability, Linear Regression, Curve Fitting,Īnd Machine Learning sections. The examples below demonstrate univariate and bivariate scatter These larger samples can be used to build reliable statistical models that describe large data sets (billions of documents) with sub-second performance. The visualization examples below use small random samples, but Solr’s random sampling provides sub-second response times on sample sizes of over 200,000. Samples that can be used to infer information about the larger result set. This allows for fast visualization, statistical analysis, and modeling of The random function returns a random sample from a distributed search result set. This simple function is useful for exploring the fields in the data and understanding how to start refining the search criteria. This returns a result set of 10 records with all fields. In the example the search function is passed only the name of the collection being searched. Zeppelin-Solr sends the seach(logs) call to the /stream handler and displays the results The search function can be used to search a SolrCloud collection and return aīelow is an example of the most basic search function called from the Zeppelin-Solr interpreter. Visualization and statistical analysis: searching, sampling Provides an overview of the key functions for retrieving data for ![]() Data is the indispensable factor in statistical analysis. ![]()
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