From fb116d110063bce525e73c21eabd00d8f748b973 Mon Sep 17 00:00:00 2001 From: mine-cetinkaya-rundel Date: Mon, 8 May 2023 23:17:09 -0400 Subject: [PATCH 1/2] Draft of Quarto/HTML cheatsheet for sparklyr --- html/sparklyr/sparklyr.qmd | 468 +++++++++++++++++++++++++++++++++++++ 1 file changed, 468 insertions(+) create mode 100644 html/sparklyr/sparklyr.qmd diff --git a/html/sparklyr/sparklyr.qmd b/html/sparklyr/sparklyr.qmd new file mode 100644 index 00000000..36915b7f --- /dev/null +++ b/html/sparklyr/sparklyr.qmd @@ -0,0 +1,468 @@ +--- +title: "Data Science in Spark with Sparklyr :: Cheat Sheet" +format: + html: + toc: true + highlight-style: a11y-dark +editor: visual +--- + +## Intro + +sparklyr is an R interface for **Apache Spark**, it provides a complete **dplyr** backend and the option to query directly using **Spark SQL** statement. With **sparklyr**, you can orchestrate distributed machine learning using either **Spark's MLlib** or **H2O** Sparkling Water. Starting with **version 1.044**, **RStudio Desktop**, **Server and Pro include integrated support for the sparklyr package**. You can create and manage connections to Spark clusters and local Spark instances from inside the IDE. + +### RStudio Integrates with sparklyr + +TODO Screenshots + +Expand to read about the sparklyr features in the RStudio IDE. + +#### Sparklyr features in the RStudio IDE + +- Open connection log +- Disconnect +- Open the Spark UI +- Spark & Hive Tables +- Preview 1K rows + +## Cluster Deployment + +In a managed cluster, the driver node (RStudio, Spark, Hive) connects to the cluster manager (Yarn, Mesos) which connects to the worker nodes (Spark). + +In a stand alone cluster the driver node (RStudio, Spark) connects directly to the worker nodes (Spark). + +## Data Science Toolchain with Spark + sparklyr + +1. Import + - Export an R DataFrame + + - Read a file + + - Read existing Hive table +2. Tidy/Wrangle + - dplyr verb + + - Direct Spark SQL (DBI) + + - SDF function (Scala API) +3. Understand + - Transform - Transformer function + + - Visualize - Collect data into R for plotting + + - Model - Spark MLlib and H2O Extension +4. Communicate + - Collect data into R + + - Share plots, documents, and apps + +## Getting Started + +### Local Mode (no cluster required) + +1. Install a local version of Spark: + + ```{r} + #| eval: false + spark_install("2.0.1") + ``` + +2. Open a connection: + + ```{r} + #| eval: false + sc <- spark_connect(master = "local") + ``` + +### On a Mesos Managed Cluster + +1. Install RStudio Server or Pro on one of the existing nodes + +2. Locate path to the cluster's Spark directory + +3. Open a connection + + ```{r} + #| eval: false + + spark_connect(master = "[mesos URL]", + version = "1.6.2", + spark_home = [Cluster’s Spark path]) + ``` + +### Using Livy (Experimental) + +1. The Livy REST application should be running on the cluster + +2. Connect to the cluster + + ```{r} + #| eval: false + + sc <- spark_connect(method = "livy", + master = "http://host:port") + ``` + +### On a Yarn Managed Cluster + +1. Install RStudio Server or RStudio Pro on one of the existing nodes, preferably an edge node + +2. Locate path to the cluster's Spark Home Directory, it normally is `/usr/lib/spark` + +3. Open a connection + + ```{r} + #| eval: false + + spark_connect(master="yarn-client", + version = "1.6.2", + spark_home = [Cluster’s Spark path]) + ``` + +### On a Spark Standaline Cluster + +1. Install RStudio Server or RStudio Pro on one of the existing nodes or a server in the same LAN + +2. Install a local version of Spark: + + ```{r} + #| eval: false + spark_install(version = "2.0.1") + ``` + +3. Open a connection + + ```{r} + #| eval: false + spark_connect(master="spark:// host:port", + version = "2.0.1", + spark_home = spark_home_dir()) + ``` + +## Tuning Spark + +### Example Configuration + +```{r} +#| eval: false + +config <- spark_config() +config$spark.executor.cores <- 2 +config$spark.executor.memory <- "4G" +sc <- spark_connect (master="yarn-client", config = config, version = "2.0.1") +``` + +### Important Tuning Parameters (with defaults) + +- `spark.yarn.am.cores` +- `spark.yarn.am.memory`: 512m +- `spark.network.timeout`: 120s +- `spark.executor.memory`: 1g +- `spark.executor.cores`: 1 +- `spark.executor.instances` +- `spark.executor.extraJavaOptions` +- `spark.executor.heartbeatInterval`: 10s +- `sparklyr.shell.executor-memory` +- `sparklyr.shell.driver-memory` + +## Using sparklyr + +```{r} +#| eval: false + +library(sparklyr) +library(dplyr) +library(ggplot2) +library(tidyr) +set.seed(100) + +#Install Spark locally +spark_install("2.0.1") + +# Connect to local version +sc <- spark_connect(master = "local") + +# Copy data to Spark memory +import_iris <- copy_to(sc, + iris, + "spark_iris", + overwrite = TRUE) + +# Partition data +partition_iris <- sdf_partition(import_iris, + training = 0.5, + testing = 0.5) + +#Create a hive metadata for each partition + +sdf_register(partition_iris, + c("spark_iris_training", "spark_iris_test")) + +spark_connect(master = "[mesos URL]", + version = "1.6.2", spark_home = [Cluster’s Spark path]) + +tidy_iris <- tbl(sc, "spark_iris_training") %>% + select(Species, Petal_Length, Petal_Width) + +# Spark ML Decision Tree Model +model_iris <- tidy_iris %>% + ml_decision_tree(response = "Species", + features = c("Petal_Length", "Petal_Width")) + +# Create reference to Spark table +test_iris <- tbl(sc, "spark_iris_test") + +# Bring data back into R memory for plotting +pred_iris <- sdf_predict(model_iris, test_iris) %>% + collect + +pred_iris %>% inner_join(data.frame(prediction = 0:2, lab = model_iris$model.parameters$labels)) %>% + ggplot(aes(Petal_Length, Petal_Width, col = lab)) + geom_point() + +# Disconnect +spark_disconnect(sc) +``` + + + +## Reactivity + +### Copy a Data Frame Into Spark + +- `sdf_copy_to(sc, x, name, memory, repartition, overwrite)` + + ```{r} + #| eval: false + + sdf_copy_to(sc, iris, "spark_iris") + ``` + +### Import Into Spark From a File + +Arguments that apply to all functions: `sc`, `name`, `path`, `options = list()`, `repartition = 0`, `memory = TRUE`, `overwrite = TRUE` + +- `spark_read_csv(header = TRUE, columns = NULL, infer_schema = TRUE, delimiter = ",", quote = "\"", escape = "\\", charset = "UTF-8", null_value = NULL)` + +- `spark_read_json()` + +- `spark_read_parquet()` + +### Spark SQL Commands + +- `DBI::dbWriteTable(conn, value)` + + ```{r} + #| eval: false + + DBI::dbWriteTable(sc, "spark_iris", iris) + ``` + +### From a Table in Hive + +- `tbl_cache(sc, name, force = TRUE)`: Loads the table into memory + + ```{r} + #| eval: false + my_var <- tbl_cache(sc, name= "hive_iris") + ``` + +- `dplyr::tbl(scr, ...)`: Creates a reference to the table without loading it into memory + + ```{r} + #| eval: false + my_var <- dplyr::tbl(sc, name= "hive_iris") + ``` + +## Wrangle + +### Spark SQL via dplyer Verbs + +- Translates into Spark SQL statements: + + ```{r} + #| eval: false + + my_table <- my_var %>% + filter(Species=="setosa") %>% + sample_n(10) + ``` + +### Direct Spark SQL Commands + +- `DBI::dbGetQuery(conn, statement)` + + ```{r} + #| eval: false + my_table <- DBI::dbGetQuery(sc, "SELECT * FROM iris LIMIT 10") + ``` + +### Scala API via SDF Functions + +- `sdf_mutate(.data)`: Works like dplyr mutate function + +- `sdf_partition(x, ..., weights = NULL, seed = sample (.Machine$integer.max, 1))` + + ```{r} + #| eval: false + sdf_partition(x, training = 0.5, test = 0.5) sdf_register(x, name = NULL) + ``` + +- `sdf_register(x, name = NULL)`: Gives a Spark DataFrame a table name + +- `sdf_sample(x, fraction = 1, replacement = TRUE, seed = NULL)` + +- `sdf_sort(x, columns)`: Sorts by \>=1 columns in ascending order + +- `sdf_with_unique_id(x, id = "id")` + +- `sdf_predict(object, newdata)`: Spark DataFrame with predicted values + +### ML Transformers + +Example: + +```{r} +#| eval: false +ft_binarizer(my_table, + input.col="Petal_Length", + output.col="petal_large", + threshold=1.2) +``` + +Arguments that apply to all functions: `x`, `input.col = NULL`, `output.col = NULL` + +- `ft_binarizer(threshold = 0.5)`: Assigned values based on threshold + +- `ft_bucketizer(splits)`: Numeric column to discretized column + +- `ft_discrete_cosine_transform(inverse = FALSE)`: Time domain to frequency domain + +- `ft_elementwise_product(scaling.col)`: Element-wise product between 2 cols + +- `ft_index_to_string()`: Index labels back to label as strings + +- `ft_one_hot_encoder()`: Continuous to binary vectors + +- `ft_quantile_discretizer(n.buckets=5L)`: Continuous to binned categorical values + +- `ft_sql_transformer(sql)` + +- `ft_string_indexer(params = NULL)`: Column of labels into a column of label indices + +- `ft_vector_assembler()`: Combine vectors into single row-vector + +## Visulize & Communicate + +### Download Data to R Memory + +Example: + +```{r} +#| eval: false + +r_table <- collect(my_table) +plot(Petal_Width~Petal_Length, + data=r_table) +``` + +- `dplyr::collect(x)`: Download a Spark DataFrame to an R DataFrame + +- `sdf_read_column(x, column)`: Returns contents of a single column to R + +### Save From Spark to File System + +Arguments that apply to all functions: `x`, `path` + +- `spark_read_csv( header = TRUE, delimiter = ",", quote = "\"", escape = "\\", charset = "UTF-8", null_value = NULL)` + +- `spark_read_json(mode = NULL)` + +- `spark_read_parquet(mode = NULL)` + +## Reading & Writing from Apache Spark + +Write to Spark, from R with `sdf_copy_to()`, `dplyr::copy_to()`, or `DBI::sbWriteTable()`. + +Read from Spark, to R with `sdf_collect()`, `dplyr::collect()`, `sdf_read_column`. + +------------------------------------------------------------------------ + +Write to Spark, from Hive with `tbl_cache()` or `dplyr::tbl()`. + +------------------------------------------------------------------------ + +Write to Spark from the file system with `spark_read_()`. + +Read from Spark to the file system with `spark_write_()`. + +## Extensions + +Create an R package that calls the full Spark API & provide interfaces to Park packages. + +### Core Types + +- `spark_connection()`: Connection between R and the Spark shell process + +- `spark_jobj()`: Instance og a remote Spark object + +- `spark_dataframe()`: Instance of a remote Spark DataFrame object + +### Call Spark From R + +- `invoke()`: Call a method on a Java object + +- `invoke_new()`: Create a new object by invoking a constructor + +- `invoke_static()`: Call a static method on an object + +### Machine Learning Extensions + +- `ml_create_dummy_variables()` + +- `ml_prepare_dataframe()` + +- `ml_prepare_response_features_intercept()` + +- `ml_options()` + +- `ml_model()` + +## Model (MLlib) + +Example: + +```{r} +#| eval: false +ml_decision_tree(my_table, + response = "Species", features = c("Petal_Length" , "Petal_Width")) +``` + +- `ml_als_factorization(x, user.column = "user", rating.column = "rating", item.column = "item", rank = 10L, regularization.parameter = 0.1, iter.max = 10L, ml.options = ml_options())` + +- `ml_decision_tree(x, response, features, max.bins = 32L, max.depth = 5L, type = c("auto", "regression", "classification"), ml.options = ml_options())`: Same options for: ml_gradient_boosted_trees + +- `ml_generalized_linear_regression(x, response, features, intercept = TRUE, family = gaussian(link = "identity"), iter.max = 100L, ml.options = ml_options())` + +- `ml_kmeans(x, centers, iter.max = 100, features = dplyr::tbl_vars(x), compute.cost = TRUE, tolerance = 1e-04, ml.options = ml_options())` + +- `ml_lda(x, features = dplyr::tbl_vars(x), k = length(features), alpha = (50/k) + 1, beta = 0.1 + 1, ml.options = ml_options())` + +- `ml_linear_regression(x, response, features, intercept = TRUE, alpha = 0, lambda = 0, iter.max = 100L, ml.options = ml_options())`: Same options for: ml_logistic_regression + +- `ml_multilayer_perceptron(x, response, features, layers, iter.max = 100, seed = sample(.Machine$integer.max, 1), ml.options = ml_options())` + +- `ml_naive_bayes(x, response, features, lambda = 0, ml.options = ml_options())` + +- `ml_one_vs_rest(x, classifier, response, features, ml.options = ml_options())` + +- `ml_pca(x, features = dplyr::tbl_vars(x), ml.options = ml_options())` + +- `ml_random_forest(x, response, features, max.bins = 32L, max.depth = 5L, num.trees = 20L, type = c("auto", "regression", "classification"), ml.options = ml_options())` + +- `ml_survival_regression(x, response, features, intercept = TRUE,censor = "censor", iter.max = 100L, ml.options = ml_options())` + +- `ml_binary_classification_eval(predicted_tbl_spark, label, score, metric = "areaUnderROC")` + +- `ml_classification_eval(predicted_tbl_spark, label, predicted_lbl, metric = "f1")` + +- `ml_tree_feature_importance(sc, model)` From e2ee7e9a7658be44917373a08ae07c3e65a9f5b5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mine=20=C3=87etinkaya-Rundel?= Date: Fri, 31 May 2024 00:54:10 -0400 Subject: [PATCH 2/2] Add html version of updated sparklyr cheatsheet --- .../html/sparklyr/execute-results/html.json | 8 +- .../sparklyr/execute-results/html.json | 15 + html/images/sparklyr-ds-workflow.png | Bin 0 -> 88434 bytes html/images/sparklyr-import.png | Bin 0 -> 241348 bytes html/images/sparklyr-save-retrieve.png | Bin 0 -> 44831 bytes html/sparklyr.qmd | 374 +++++++++++++++++- 6 files changed, 389 insertions(+), 8 deletions(-) create mode 100644 _freeze/html/sparklyr/sparklyr/execute-results/html.json create mode 100644 html/images/sparklyr-ds-workflow.png create mode 100644 html/images/sparklyr-import.png create mode 100644 html/images/sparklyr-save-retrieve.png diff --git a/_freeze/html/sparklyr/execute-results/html.json b/_freeze/html/sparklyr/execute-results/html.json index a19335b4..4b8c73d8 100644 --- a/_freeze/html/sparklyr/execute-results/html.json +++ b/_freeze/html/sparklyr/execute-results/html.json @@ -1,11 +1,9 @@ { - "hash": "dfa5d20431126ba509f25c2253692592", + "hash": "8ebe6bf2cb68f3277c06277a66ff6e8c", "result": { "engine": "knitr", - "markdown": "---\ntitle: \"Data science in Spark with sparklyr :: Cheatsheet\"\ndescription: \" \"\nimage-alt: \"\"\nexecute:\n eval: true\n output: false\n warning: false\n---\n\n::: {.cell .column-margin}\n\"Hex\n

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\n* Chinese\n* Chinese\n* German\n* Japanese\n* Spanish\n:::\n\n\n\n\n\n\nHTML version coming soon!\nThe PDF is available to download [here](../sparklyr.pdf).\n", - "supporting": [ - "sparklyr_files" - ], + "markdown": "---\ntitle: \"Data science in Spark with sparklyr :: Cheatsheet\"\ndescription: \" \"\nimage-alt: \"\"\nexecute:\n eval: false\n output: false\n warning: false\n---\n\n::: {.cell .column-margin}\n\n:::\n\n\n\n\n\n![](images/sparklyr-ds-workflow.png)\n\n# Connect\n\n## Databricks Connect (v2)\n\nSupported in Databricks Connect v2\n\n1. Open your .Renviron file: `usethis::edit_r_environ()`\n\n2. In the .Renviron file add your Databricks Host Url and Token (PAT):\n\n - `DATABRICKS_HOST = \\[Your Host URL\\]`\n - `DATABRICKS_TOKEN = \\[Your PAT\\]`\n\n3. Install extension: `install.packages(\"pysparklyr\")`\n\n4. Open connection:\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsc <- spark_connect(\n cluster_id = \"[Your cluster’s ID]\",\n method = \"databricks_connect\"\n)\n```\n:::\n\n\n\n## Standalone cluster\n\n1. Install RStudio Server on one of the existing nodes or a server in the same LAN\n\n2. Open a connection\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nspark_connect(\n master=\"spark://host:port\",\n version = \"3.2\",\n spark_home = [path to Spark]\n)\n```\n:::\n\n\n\n## Yarn client\n\n1. Install RStudio Server on an edge node\n\n2. Locate path to the clusterʼs Spark Home Directory, it normally is `\"/usr/lib/spark\"`\n\n3. Basic configuration example\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nconf <- spark_config()\nconf$spark.executor.memory <- \"300M\"\nconf$spark.executor.cores <- 2\nconf$spark.executor.instances <- 3\nconf$spark.dynamicAllocation.enabled<-\"false\"\n```\n:::\n\n\n\n4. Open a connection\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsc <- spark_connect(\n master = \"yarn\",\n spark_home = \"/usr/lib/spark/\",\n version = \"2.1.0\", config = conf\n)\n```\n:::\n\n\n\n## Yarn cluster\n\n1. Make sure to have copies of the **yarn-site.xml** and **hive-site.xml** files in the RStudio Server\n\n2. Point environment variables to the correct paths\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nSys.setenv(JAVA_HOME=\"[Path]\")\nSys.setenv(SPARK_HOME =\"[Path]\")\nSys.setenv(YARN_CONF_DIR =\"[Path]\")\n```\n:::\n\n\n\n3. Open a connection\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsc <- spark_connect(master = \"yarn-cluster\")\n```\n:::\n\n\n\n## Kubernetes\n\n1. Use the following to obtain the Host and Port `system2(\"kubectl\", \"cluster-info\")`\n\n2. Open a connection\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsc <- spark_connect(\n config = spark_config_kubernetes(\n \"k8s://https://[HOST]>:[PORT]\",\n account = \"default\",\n image = \"docker.io/owner/repo:version\"\n )\n )\n```\n:::\n\n\n\n## Local mode\n\nNo cluster required.\nUse for learning purposes only\n\n1. Install a local version of Spark: spark_install()\n\n2. Open a connection\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsc <- spark_connect(master=\"local\") \n```\n:::\n\n\n\n## Cloud\n\n**Azure** - `spark_connect(method = \"synapse\")`\n\n**Qubole** - `spark_connect(method = \"qubole\")`\n\n# Import\n\n![](images/sparklyr-import.png){fig-alt=\"R push complete to Spark back to R collecting results. Also import from Source to Spark.\" fig-align=\"center\" width=\"501\"}\n\nImport data into Spark, not R\n\n## Read a file into Spark\n\n**Arguments that apply to all functions:**\n\nsc, name, path, options=list(), repartition=0, memory=TRUE, overwrite=TRUE\n\n- CSV: `spark_read_csv(header = TRUE, columns = NULL, infer_schema = TRUE, delimiter = \",\", quote= \"\\\"\", escape = \"\\\\\", charset = \"UTF-8\", null_value = NULL)`\n- JSON: `spark_read_json()`\n- PARQUET: `spark_read_parquet()`\n- TEXT: `spark_read_text()`\n- DELTA: `spark_read_delta()`\n\n## From a table\n\n- `dplyr::tbl(scr, ...)` - Creates a reference to the table without loading its data into memory\n\n- `dbplyr::in_catalog()` - Enables a three part table address\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nx <- tbl(sc,in_catalog(\"catalog\", \"schema\", \"table\"))\n```\n:::\n\n\n\n## R data frame into Spark\n\nSupported in Databricks Connect v2\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ndplyr::copy_to(dest, df, name)\n```\n:::\n\n\n\nApache Arrow accelerates data transfer between R and Spark.\nTo use, simply load the library\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(sparklyr)\nlibrary(arrow)\n```\n:::\n\n\n\n# Wrangle\n\n## dplyr verbs\n\nSupported in Databricks Connect v2\n\nTranslates into Spark SQL statements\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncopy_to(sc, mtcars) %>%\n mutate(trm = ifelse(am == 0, \"auto\", \"man\")) %>%\n group_by(trm) %>%\n summarise_all(mean)\n```\n:::\n\n\n\n## tidyr\n\n- `pivot_longer()` - Collapse several columns into two.\n (Supported in Databricks Connect v2)\n\n- `pivot_wider()` - Expand two columns into several.\n (Supported in Databricks Connect v2)\n\n- `nest()` / `unnest()` - Convert groups of cells into list-columns, and vice versa.\n\n- `unite()` / `separate()` - Split a single column into several columns, and vice versa.\n\n- `fill()` - Fill NA with the previous value.\n\n## Feature transformers\n\n- `ft_binarizer()` - Assigned values based on threshold\n- `ft_bucketizer()` - Numeric column to discretized column\n- `ft_count_vectorizer()` - Extracts a vocabulary from document\n- `ft_discrete_cosine_transform()` - 1D discrete cosine transform of a real vector\n- `ft_elementwise_product()` - Element- wise product between 2 cols\n- `ft_hashing_tf()` - Maps a sequence of terms to their term frequencies using the hashing trick.\n- `ft_idf()` - Compute the Inverse Document Frequency (IDF) given a collection of documents.\n- `ft_imputer()` - Imputation estimator for completing missing values, uses the mean or the median of the columns.\n- `ft_index_to_string()` - Index labels back to label as strings\n- `ft_interaction()` - Takes in Double and Vector columns and outputs a flattened vector of their feature interactions.\n- `ft_max_abs_scaler()` - Rescale each feature individually to range \\[-1, 1\\] (Supported in Databricks Connect v2)\n- `ft_min_max_scaler()` - Rescale each feature to a common range \\[min, max\\] linearly\n- `ft_ngram()` - Converts the input array of strings into an array of n-grams\n- `ft_bucketed_random_projection_lsh()`\n- `ft_minhash_lsh()` - Locality Sensitive Hashing functions for Euclidean distance and Jaccard distance (MinHash)\n- `ft_normalizer()` - Normalize a vector to have unit norm using the given p-norm\n- `ft_one_hot_encoder()` - Continuous to binary vectors\n- `ft_pca()` - Project vectors to a lower dimensional space of top k principal components\n- `ft_quantile_discretizer()` - Continuous to binned categorical values.\n- `ft_regex_tokenizer()` - Extracts tokens either by using the provided regex pattern to split the text\n- `ft_robust_scaler()` - Removes the median and scales according to standard scale\n- `ft_standard_scaler()` - Removes the mean and scaling to unit variance using column summary statistics (Supported in Databricks Connect v2)\n- `ft_stop_words_remover()` - Filters out stop words from input\n- `ft_string_indexer()` - Column of labels into a column of label indices.\n- `ft_tokenizer()` - Converts to lowercase and then splits it by white spaces\n- `ft_vector_assembler()` - Combine vectors into single row-vector\n- `ft_vector_indexer()` - Indexing categorical feature columns in a dataset of Vector\n- `ft_vector_slicer()` - Takes a feature vector and outputs a new feature vector with a subarray of the original features\n- `ft_word2vec()` - Word2Vec transforms a word into a code\n\n\n\n# Visualize\n\n## dplyr + ggplot2\n\nSupported in Databricks Connect v2\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\ncopy_to(sc, mtcars) %>%\n group_by(cyl) %>%\n summarise(mpg_m = mean(mpg)) %>% # Summarize in Spark\n collect() %>% # Collect results in R\n ggplot() +\n geom_col(aes(cyl, mpg_m)) # Create plot\n```\n:::\n\n\n\n# Modeling\n\n## Regression\n\n- `ml_linear_regression()` - Linear regression.\n- `ml_aft_survival_regression()` - Parametric survival regression model named accelerated failure time (AFT) model.\n- `ml_generalized_linear_regression()` - GLM.\n- `ml_isotonic_regression()` - Uses parallelized pool adjacent violators algorithm.\n- `ml_random_forest_regressor()` - Regression using random forests.\n\n## Classification\n\n- `ml_linear_svc()` - Classification using linear support vector machines.\n- `ml_logistic_regression()` - Logistic regression. (Supported in Databricks Connect v2)\n- `ml_multilayer_perceptron_classifier()` - Based on the Multilayer Perceptron.\n- `ml_naive_bayes()` - It supports Multinomial NB which can handle finitely supported discrete data.\n- `ml_one_vs_rest()` - Reduction of Multiclass, performs reduction using one against all strategy.\n\n## Tree\n\n- `ml_decision_tree_classifier()`, `ml_decision_tree()`, \\`ml_decision_tree_regressor(). - Classification and regression using decision trees.\n- `ml_gbt_classifier()`, `ml_gradient_boosted_trees()`, `ml_gbt_regressor()` - Binary classification and regression using gradient boosted trees.\n- `ml_random_forest_classifier()` - Classification and regression using random forests.\n- `ml_feature_importances()`, `ml_tree_feature_importance()` - Feature Importance for Tree Models.\n\n## Clustering\n\n- `ml_bisecting_kmeans()` - A bisecting k-means algorithm based on the paper.\n- `ml_lda()`, `ml_describe_topics()`, `ml_log_likelihood()`, `ml_log_perplexity()`, `ml_topics_matrix()` - LDA topic model designed for text documents.\n- `ml_gaussian_mixture()` - Expectation maximization for multivariate Gaussian Mixture Models (GMMs).\n- `ml_kmeans()`, `ml_compute_cost()`, `ml_compute_silhouette_measure()` - Clustering with support for k-means.\n- `ml_power_iteration()` - For clustering vertices of a graph given pairwise similarities as edge properties.\n\n## Recommendation\n\n- `ml_als()`, `ml_recommend()` - Recommendation using Alternating Least Squares matrix factorization.\n\n## Evaluation\n\n- `ml_clustering_evaluator()` - Evaluator for clustering.\n- `ml_evaluate()` - Compute performance metrics.\n- `ml_binary_classification_evaluator()`, `ml_binary_classification_eval()`, `ml_classification_eval()` - A set of functions to calculate performance metrics for prediction models.\n\n## Frequent pattern\n\n- `ml_fpgrowth()`, `ml_association_rules()`, `ml_freq_itemsets()` - A parallel FP-growth algorithm to mine frequent itemsets.\n- `ml_freq_seq_patterns()`, `ml_prefixspan()` - PrefixSpan algorithm for mining frequent itemsets.\n\n## Stats\n\n- `ml_summary()` - Extracts a metric from the summary object of a Spark ML model.\n- `ml_corr()` - Compute correlation matrix.\n\n## Feature\n\n- `ml_chisquare_test(x,features,label)` - Pearson's independence test for every feature against the label.\n- `ml_default_stop_words()` - Loads the default stop words for the given language.\n\n## Utilities\n\n- `ml_call_constructor()` - Identifies the associated sparklyr ML constructor for the JVM.\n- `ml_model_data()` - Extracts data associated with a Spark ML model.\n- `ml_standardize_formula()` - Generates a formula string from user inputs.\n- `ml_uid()` - Extracts the UID of an ML object.\n\n# ML pipelines\n\nEasily create a formal Spark Pipeline models using R.\nSave the Pipeline in native Sacala.\nIt will have **no dependencies** on R.\n\n## Initialize and train\n\nSupported in Databricks Connect v2\n\n- `ml_pipeline()` - Initializes a new Spark Pipeline.\n- `ml_fit()` - Trains the model, outputs a Spark Pipeline Model.\n\n## Save and retrieve\n\nSupported in Databricks Connect v2\n\n- `ml_save()` - Saves into a format that can be read by Scala and PySpark.\n- `ml_read()` - Reads Spark object into sparklyr.\n\n![](images/sparklyr-save-retrieve.png){fig-alt=\"ml_pipeline() to ft_dplyr_transformer to ft_bucketizer() to ml_linear_regression() to ml_fit() to ml_save().\" fig-align=\"center\" width=\"501\"}\n\n[spark.posit.co/guides/pipelines](https://spark.posit.co/guides/pipelines)\n\n# Distributed R\n\nSupported in Databricks Connect v2\n\nRun arbitrary R code at scale inside your cluster with spark_apply().\nUseful when there you need functionality only available in R, and to solve 'embarrassingly parallel problems'.\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nspark_apply(\n x, f, \n columns = NULL, memory = TRUE, group_by = NULL, \n name = NULL, barrier = NULL, fetch_result_as_sdf = TRUE\n )\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\ncopy_to(sc, mtcars) %>%\n spark_apply(\n nrow, # R only function\n group_by = \"am\", \n columns = \"am double, x long\"\n )\n```\n:::\n\n\n\n------------------------------------------------------------------------\n\nCC BY SA Posit Software, PBC • [info\\@posit.co](mailto:info@posit.co) • [posit.co](https://posit.co)\n\nLearn more at [spark.posit.co](https://spark.posit.co/) and [therinspark.com](https://therinspark.com/).\n\nUpdated: 2024-05.\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\npackageVersion(\"sparklyr\")\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n[1] '1.8.6'\n```\n\n\n:::\n:::\n\n\n\n------------------------------------------------------------------------\n", + "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" ], diff --git a/_freeze/html/sparklyr/sparklyr/execute-results/html.json b/_freeze/html/sparklyr/sparklyr/execute-results/html.json new file mode 100644 index 00000000..4b8e395b --- /dev/null +++ b/_freeze/html/sparklyr/sparklyr/execute-results/html.json @@ -0,0 +1,15 @@ +{ + "hash": "319f3fddc68fd7b995e659573b518077", + "result": { + "engine": "knitr", + "markdown": "---\ntitle: \"Data Science in Spark with Sparklyr :: Cheat Sheet\"\nformat: \n html:\n toc: true\n highlight-style: a11y-dark\neditor: visual\n---\n\n\n\n\n\n## Intro\n\nsparklyr is an R interface for **Apache Spark**, it provides a complete **dplyr** backend and the option to query directly using **Spark SQL** statement. With **sparklyr**, you can orchestrate distributed machine learning using either **Spark's MLlib** or **H2O** Sparkling Water. Starting with **version 1.044**, **RStudio Desktop**, **Server and Pro include integrated support for the sparklyr package**. You can create and manage connections to Spark clusters and local Spark instances from inside the IDE.\n\n### RStudio Integrates with sparklyr\n\nTODO Screenshots\n\nExpand to read about the sparklyr features in the RStudio IDE.\n\n#### Sparklyr features in the RStudio IDE\n\n- Open connection log\n- Disconnect\n- Open the Spark UI\n- Spark & Hive Tables\n- Preview 1K rows\n\n## Cluster Deployment\n\nIn a managed cluster, the driver node (RStudio, Spark, Hive) connects to the cluster manager (Yarn, Mesos) which connects to the worker nodes (Spark).\n\nIn a stand alone cluster the driver node (RStudio, Spark) connects directly to the worker nodes (Spark).\n\n## Data Science Toolchain with Spark + sparklyr\n\n1. Import\n - Export an R DataFrame\n\n - Read a file\n\n - Read existing Hive table\n2. Tidy/Wrangle\n - dplyr verb\n\n - Direct Spark SQL (DBI)\n\n - SDF function (Scala API)\n3. Understand\n - Transform - Transformer function\n\n - Visualize - Collect data into R for plotting\n\n - Model - Spark MLlib and H2O Extension\n4. Communicate\n - Collect data into R\n\n - Share plots, documents, and apps\n\n## Getting Started\n\n### Local Mode (no cluster required)\n\n1. Install a local version of Spark:\n\n\n\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n spark_install(\"2.0.1\")\n ```\n :::\n\n\n\n\n\n2. Open a connection:\n\n\n\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n sc <- spark_connect(master = \"local\")\n ```\n :::\n\n\n\n\n\n### On a Mesos Managed Cluster\n\n1. Install RStudio Server or Pro on one of the existing nodes\n\n2. Locate path to the cluster's Spark directory\n\n3. Open a connection\n\n\n\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n spark_connect(master = \"[mesos URL]\",\n version = \"1.6.2\", \n spark_home = [Cluster’s Spark path])\n ```\n :::\n\n\n\n\n\n### Using Livy (Experimental)\n\n1. The Livy REST application should be running on the cluster\n\n2. Connect to the cluster\n\n\n\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n sc <- spark_connect(method = \"livy\", \n master = \"http://host:port\")\n ```\n :::\n\n\n\n\n\n### On a Yarn Managed Cluster\n\n1. Install RStudio Server or RStudio Pro on one of the existing nodes, preferably an edge node\n\n2. Locate path to the cluster's Spark Home Directory, it normally is `/usr/lib/spark`\n\n3. Open a connection\n\n\n\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n spark_connect(master=\"yarn-client\", \n version = \"1.6.2\", \n spark_home = [Cluster’s Spark path])\n ```\n :::\n\n\n\n\n\n### On a Spark Standaline Cluster\n\n1. Install RStudio Server or RStudio Pro on one of the existing nodes or a server in the same LAN\n\n2. Install a local version of Spark:\n\n\n\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n spark_install(version = \"2.0.1\")\n ```\n :::\n\n\n\n\n\n3. Open a connection\n\n\n\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n spark_connect(master=\"spark:// host:port\",\n version = \"2.0.1\", \n spark_home = spark_home_dir())\n ```\n :::\n\n\n\n\n\n## Tuning Spark\n\n### Example Configuration\n\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nconfig <- spark_config() \nconfig$spark.executor.cores <- 2\nconfig$spark.executor.memory <- \"4G\" \nsc <- spark_connect (master=\"yarn-client\", config = config, version = \"2.0.1\")\n```\n:::\n\n\n\n\n\n### Important Tuning Parameters (with defaults)\n\n- `spark.yarn.am.cores`\n- `spark.yarn.am.memory`: 512m\n- `spark.network.timeout`: 120s\n- `spark.executor.memory`: 1g\n- `spark.executor.cores`: 1\n- `spark.executor.instances`\n- `spark.executor.extraJavaOptions`\n- `spark.executor.heartbeatInterval`: 10s\n- `sparklyr.shell.executor-memory`\n- `sparklyr.shell.driver-memory`\n\n## Using sparklyr\n\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(sparklyr)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(tidyr)\nset.seed(100)\n\n#Install Spark locally\nspark_install(\"2.0.1\")\n\n# Connect to local version\nsc <- spark_connect(master = \"local\")\n\n# Copy data to Spark memory\nimport_iris <- copy_to(sc, \n iris, \n \"spark_iris\", \n overwrite = TRUE)\n\n# Partition data\npartition_iris <- sdf_partition(import_iris,\n training = 0.5, \n testing = 0.5)\n\n#Create a hive metadata for each partition\n\nsdf_register(partition_iris,\n c(\"spark_iris_training\", \"spark_iris_test\"))\n \nspark_connect(master = \"[mesos URL]\", \n version = \"1.6.2\", spark_home = [Cluster’s Spark path])\n\ntidy_iris <- tbl(sc, \"spark_iris_training\") %>% \n select(Species, Petal_Length, Petal_Width)\n\n# Spark ML Decision Tree Model\nmodel_iris <- tidy_iris %>%\n ml_decision_tree(response = \"Species\",\n features = c(\"Petal_Length\", \"Petal_Width\"))\n\n# Create reference to Spark table\ntest_iris <- tbl(sc, \"spark_iris_test\")\n\n# Bring data back into R memory for plotting\npred_iris <- sdf_predict(model_iris, test_iris) %>% \n collect\n\npred_iris %>% inner_join(data.frame(prediction = 0:2, lab = model_iris$model.parameters$labels)) %>%\n ggplot(aes(Petal_Length, Petal_Width, col = lab)) + geom_point()\n\n# Disconnect\nspark_disconnect(sc)\n```\n:::\n\n\n\n\n\n\n\n## Reactivity\n\n### Copy a Data Frame Into Spark\n\n- `sdf_copy_to(sc, x, name, memory, repartition, overwrite)`\n\n\n\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n sdf_copy_to(sc, iris, \"spark_iris\")\n ```\n :::\n\n\n\n\n\n### Import Into Spark From a File\n\nArguments that apply to all functions: `sc`, `name`, `path`, `options = list()`, `repartition = 0`, `memory = TRUE`, `overwrite = TRUE`\n\n- `spark_read_csv(header = TRUE, columns = NULL, infer_schema = TRUE, delimiter = \",\", quote = \"\\\"\", escape = \"\\\\\", charset = \"UTF-8\", null_value = NULL)`\n\n- `spark_read_json()`\n\n- `spark_read_parquet()`\n\n### Spark SQL Commands\n\n- `DBI::dbWriteTable(conn, value)`\n\n\n\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n DBI::dbWriteTable(sc, \"spark_iris\", iris)\n ```\n :::\n\n\n\n\n\n### From a Table in Hive\n\n- `tbl_cache(sc, name, force = TRUE)`: Loads the table into memory\n\n\n\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n my_var <- tbl_cache(sc, name= \"hive_iris\")\n ```\n :::\n\n\n\n\n\n- `dplyr::tbl(scr, ...)`: Creates a reference to the table without loading it into memory\n\n\n\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n my_var <- dplyr::tbl(sc, name= \"hive_iris\")\n ```\n :::\n\n\n\n\n\n## Wrangle\n\n### Spark SQL via dplyer Verbs\n\n- Translates into Spark SQL statements:\n\n\n\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n my_table <- my_var %>% \n filter(Species==\"setosa\") %>% \n sample_n(10)\n ```\n :::\n\n\n\n\n\n### Direct Spark SQL Commands\n\n- `DBI::dbGetQuery(conn, statement)`\n\n\n\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n my_table <- DBI::dbGetQuery(sc, \"SELECT * FROM iris LIMIT 10\")\n ```\n :::\n\n\n\n\n\n### Scala API via SDF Functions\n\n- `sdf_mutate(.data)`: Works like dplyr mutate function\n\n- `sdf_partition(x, ..., weights = NULL, seed = sample (.Machine$integer.max, 1))`\n\n\n\n\n\n ::: {.cell}\n \n ```{.r .cell-code}\n sdf_partition(x, training = 0.5, test = 0.5) sdf_register(x, name = NULL)\n ```\n :::\n\n\n\n\n\n- `sdf_register(x, name = NULL)`: Gives a Spark DataFrame a table name\n\n- `sdf_sample(x, fraction = 1, replacement = TRUE, seed = NULL)`\n\n- `sdf_sort(x, columns)`: Sorts by \\>=1 columns in ascending order\n\n- `sdf_with_unique_id(x, id = \"id\")`\n\n- `sdf_predict(object, newdata)`: Spark DataFrame with predicted values\n\n### ML Transformers\n\nExample:\n\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nft_binarizer(my_table,\n input.col=\"Petal_Length\", \n output.col=\"petal_large\", \n threshold=1.2)\n```\n:::\n\n\n\n\n\nArguments that apply to all functions: `x`, `input.col = NULL`, `output.col = NULL`\n\n- `ft_binarizer(threshold = 0.5)`: Assigned values based on threshold\n\n- `ft_bucketizer(splits)`: Numeric column to discretized column\n\n- `ft_discrete_cosine_transform(inverse = FALSE)`: Time domain to frequency domain\n\n- `ft_elementwise_product(scaling.col)`: Element-wise product between 2 cols\n\n- `ft_index_to_string()`: Index labels back to label as strings\n\n- `ft_one_hot_encoder()`: Continuous to binary vectors\n\n- `ft_quantile_discretizer(n.buckets=5L)`: Continuous to binned categorical values\n\n- `ft_sql_transformer(sql)`\n\n- `ft_string_indexer(params = NULL)`: Column of labels into a column of label indices\n\n- `ft_vector_assembler()`: Combine vectors into single row-vector\n\n## Visulize & Communicate\n\n### Download Data to R Memory\n\nExample:\n\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nr_table <- collect(my_table) \nplot(Petal_Width~Petal_Length, \n data=r_table)\n```\n:::\n\n\n\n\n\n- `dplyr::collect(x)`: Download a Spark DataFrame to an R DataFrame\n\n- `sdf_read_column(x, column)`: Returns contents of a single column to R\n\n### Save From Spark to File System\n\nArguments that apply to all functions: `x`, `path`\n\n- `spark_read_csv( header = TRUE, delimiter = \",\", quote = \"\\\"\", escape = \"\\\\\", charset = \"UTF-8\", null_value = NULL)`\n\n- `spark_read_json(mode = NULL)`\n\n- `spark_read_parquet(mode = NULL)`\n\n## Reading & Writing from Apache Spark\n\nWrite to Spark, from R with `sdf_copy_to()`, `dplyr::copy_to()`, or `DBI::sbWriteTable()`.\n\nRead from Spark, to R with `sdf_collect()`, `dplyr::collect()`, `sdf_read_column`.\n\n------------------------------------------------------------------------\n\nWrite to Spark, from Hive with `tbl_cache()` or `dplyr::tbl()`.\n\n------------------------------------------------------------------------\n\nWrite to Spark from the file system with `spark_read_()`.\n\nRead from Spark to the file system with `spark_write_()`.\n\n## Extensions\n\nCreate an R package that calls the full Spark API & provide interfaces to Park packages.\n\n### Core Types\n\n- `spark_connection()`: Connection between R and the Spark shell process\n\n- `spark_jobj()`: Instance og a remote Spark object\n\n- `spark_dataframe()`: Instance of a remote Spark DataFrame object\n\n### Call Spark From R\n\n- `invoke()`: Call a method on a Java object\n\n- `invoke_new()`: Create a new object by invoking a constructor\n\n- `invoke_static()`: Call a static method on an object\n\n### Machine Learning Extensions\n\n- `ml_create_dummy_variables()`\n\n- `ml_prepare_dataframe()`\n\n- `ml_prepare_response_features_intercept()`\n\n- `ml_options()`\n\n- `ml_model()`\n\n## Model (MLlib)\n\nExample:\n\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code}\nml_decision_tree(my_table, \n response = \"Species\", features = c(\"Petal_Length\" , \"Petal_Width\"))\n```\n:::\n\n\n\n\n\n- `ml_als_factorization(x, user.column = \"user\", rating.column = \"rating\", item.column = \"item\", rank = 10L, regularization.parameter = 0.1, iter.max = 10L, ml.options = ml_options())`\n\n- `ml_decision_tree(x, response, features, max.bins = 32L, max.depth = 5L, type = c(\"auto\", \"regression\", \"classification\"), ml.options = ml_options())`: Same options for: ml_gradient_boosted_trees\n\n- `ml_generalized_linear_regression(x, response, features, intercept = TRUE, family = gaussian(link = \"identity\"), iter.max = 100L, ml.options = ml_options())`\n\n- `ml_kmeans(x, centers, iter.max = 100, features = dplyr::tbl_vars(x), compute.cost = TRUE, tolerance = 1e-04, ml.options = ml_options())`\n\n- `ml_lda(x, features = dplyr::tbl_vars(x), k = length(features), alpha = (50/k) + 1, beta = 0.1 + 1, ml.options = ml_options())`\n\n- `ml_linear_regression(x, response, features, intercept = TRUE, alpha = 0, lambda = 0, iter.max = 100L, ml.options = ml_options())`: Same options for: ml_logistic_regression\n\n- `ml_multilayer_perceptron(x, response, features, layers, iter.max = 100, seed = sample(.Machine$integer.max, 1), ml.options = ml_options())`\n\n- `ml_naive_bayes(x, response, features, lambda = 0, ml.options = ml_options())`\n\n- `ml_one_vs_rest(x, classifier, response, features, ml.options = ml_options())`\n\n- `ml_pca(x, features = dplyr::tbl_vars(x), ml.options = ml_options())`\n\n- `ml_random_forest(x, response, features, max.bins = 32L, max.depth = 5L, num.trees = 20L, type = c(\"auto\", \"regression\", \"classification\"), ml.options = ml_options())`\n\n- `ml_survival_regression(x, response, features, intercept = TRUE,censor = \"censor\", iter.max = 100L, ml.options = ml_options())`\n\n- `ml_binary_classification_eval(predicted_tbl_spark, label, score, metric = \"areaUnderROC\")`\n\n- `ml_classification_eval(predicted_tbl_spark, label, predicted_lbl, metric = \"f1\")`\n\n- `ml_tree_feature_importance(sc, model)`\n", + "supporting": [], + "filters": [ + "rmarkdown/pagebreak.lua" + ], + "includes": {}, + "engineDependencies": {}, + "preserve": {}, + "postProcess": true + } +} \ No newline at end of file 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zJVk5s8B;2A5e|$}N^7InxOAEjM-~AIOd@uWlThCAT+`h&rY=XOV{XxN-_f&TEV-C_ z2XSk27bGdwQ;`f{^Ra>3xYlIoU^$P4NJ8~j4OK(AeJI_kkyZ;}7R^JAaoV94g zgYOijG<2D|X7p&IfiPyf7IBR@URv;h6rHc~p|Z$i+-WvF*7mnH;f+EefP~CMMq*67 zb~L?=y2UlEJ3|k+;X(xx9)7ts_07G3$aZO^{RC~YAE=jocjIs5`T$_AVdIXG=sYuR z+OiR!mJlCj2hJ)P#2sdr=AJ+Re;2-n|EZ>ez>}AK9cy7>!Wa6s_>Jq*&7Ca(W(vI_ z3P08R5;a;Gr@mF<7yGj37}G)7?y&QHx(fJ=Qp7otrX|?Zs@M4dZM!UJp(#T|cs%i5a9I^JW?-R^RvkS?~UUKBZ98N|> zgK-mOt4nGwbg_03ArIgq*Ut##NKE(eiSo21nnUK4VH&0{1u&N4yc>xp6L*90p2(-8 zdEkM12DL+}J?f)Dtipu(PfddlYtE>gEcl5_hv9l~;7E}2&)J&l`;)ob`lE#}ziPOT zA^8N^4jIYTsGd1+|5jToRdfHeH~{A#wpspw6Uj%>J(_NbK3}v>$Jz*XT+=GLAJC^! z_v1pmjq(&tfnd(Ktf04kfTyw!WVU0*4Op1jCP(y$)F_w)D+=DcW_^ivhC5>vro9Kxp(D^4(+-B=GQ}(nYWfkDotPfA?tIet|ak_ z)i^;fFnB}0@a~KVHujY0h<~t{3ScqDelue>MC(Q~*bCjyx4NkT{_yKqYxH5VI>(+n z^n&NN)@L(Z7SEmPI#JfAH~he7xZu3Szc?@GK$dJjstqGA&!nJPVSfk(3MDmk*t!&2 z<7#;0v-Z$S@$zP^etyne+b40G`H=I(fsfe16bbBVW=TP|B>mIS8SGz8u!(ghowD?uaAR98B_$p{kQ-XUc_zx$mO#IE-$_da*>RL&$$@J?dXTwtw!3Zev8{4 zL27uh#0nq&_ly79XManh9IzYWa$aW&{aKHHxtzHRmZr1&Zq+}5jlXv(ayRJ?xUuia z2N(a#|NZ$HC;_2Bp);brbbqFRO#1JKgOi0`SB|`^_^ -HTML version coming soon! -The PDF is available to download [here](../sparklyr.pdf). +![](images/sparklyr-ds-workflow.png) + +# Connect + +## Databricks Connect (v2) + +Supported in Databricks Connect v2 + +1. Open your .Renviron file: `usethis::edit_r_environ()` + +2. In the .Renviron file add your Databricks Host Url and Token (PAT): + + - `DATABRICKS_HOST = \[Your Host URL\]` + - `DATABRICKS_TOKEN = \[Your PAT\]` + +3. Install extension: `install.packages("pysparklyr")` + +4. Open connection: + +```{r} +sc <- spark_connect( + cluster_id = "[Your cluster’s ID]", + method = "databricks_connect" +) +``` + +## Standalone cluster + +1. Install RStudio Server on one of the existing nodes or a server in the same LAN + +2. Open a connection + +```{r} +spark_connect( + master="spark://host:port", + version = "3.2", + spark_home = [path to Spark] +) +``` + +## Yarn client + +1. Install RStudio Server on an edge node + +2. Locate path to the clusterʼs Spark Home Directory, it normally is `"/usr/lib/spark"` + +3. Basic configuration example + +```{r} +conf <- spark_config() +conf$spark.executor.memory <- "300M" +conf$spark.executor.cores <- 2 +conf$spark.executor.instances <- 3 +conf$spark.dynamicAllocation.enabled<-"false" +``` + +4. Open a connection + +```{r} +sc <- spark_connect( + master = "yarn", + spark_home = "/usr/lib/spark/", + version = "2.1.0", config = conf +) +``` + +## Yarn cluster + +1. Make sure to have copies of the **yarn-site.xml** and **hive-site.xml** files in the RStudio Server + +2. Point environment variables to the correct paths + +```{r} +Sys.setenv(JAVA_HOME="[Path]") +Sys.setenv(SPARK_HOME ="[Path]") +Sys.setenv(YARN_CONF_DIR ="[Path]") +``` + +3. Open a connection + +```{r} +sc <- spark_connect(master = "yarn-cluster") +``` + +## Kubernetes + +1. Use the following to obtain the Host and Port `system2("kubectl", "cluster-info")` + +2. Open a connection + +```{r} +sc <- spark_connect( + config = spark_config_kubernetes( + "k8s://https://[HOST]>:[PORT]", + account = "default", + image = "docker.io/owner/repo:version" + ) + ) +``` + +## Local mode + +No cluster required. +Use for learning purposes only + +1. Install a local version of Spark: spark_install() + +2. Open a connection + +```{r} +sc <- spark_connect(master="local") +``` + +## Cloud + +**Azure** - `spark_connect(method = "synapse")` + +**Qubole** - `spark_connect(method = "qubole")` + +# Import + +![](images/sparklyr-import.png){fig-alt="R push complete to Spark back to R collecting results. Also import from Source to Spark." fig-align="center" width="501"} + +Import data into Spark, not R + +## Read a file into Spark + +**Arguments that apply to all functions:** + +sc, name, path, options=list(), repartition=0, memory=TRUE, overwrite=TRUE + +- CSV: `spark_read_csv(header = TRUE, columns = NULL, infer_schema = TRUE, delimiter = ",", quote= "\"", escape = "\\", charset = "UTF-8", null_value = NULL)` +- JSON: `spark_read_json()` +- PARQUET: `spark_read_parquet()` +- TEXT: `spark_read_text()` +- DELTA: `spark_read_delta()` + +## From a table + +- `dplyr::tbl(scr, ...)` - Creates a reference to the table without loading its data into memory + +- `dbplyr::in_catalog()` - Enables a three part table address + +```{r} +x <- tbl(sc,in_catalog("catalog", "schema", "table")) +``` + +## R data frame into Spark + +Supported in Databricks Connect v2 + +```{r} +dplyr::copy_to(dest, df, name) +``` + +Apache Arrow accelerates data transfer between R and Spark. +To use, simply load the library + +```{r} +library(sparklyr) +library(arrow) +``` + +# Wrangle + +## dplyr verbs + +Supported in Databricks Connect v2 + +Translates into Spark SQL statements + +```{r} +copy_to(sc, mtcars) %>% + mutate(trm = ifelse(am == 0, "auto", "man")) %>% + group_by(trm) %>% + summarise_all(mean) +``` + +## tidyr + +- `pivot_longer()` - Collapse several columns into two. + (Supported in Databricks Connect v2) + +- `pivot_wider()` - Expand two columns into several. + (Supported in Databricks Connect v2) + +- `nest()` / `unnest()` - Convert groups of cells into list-columns, and vice versa. + +- `unite()` / `separate()` - Split a single column into several columns, and vice versa. + +- `fill()` - Fill NA with the previous value. + +## Feature transformers + +- `ft_binarizer()` - Assigned values based on threshold +- `ft_bucketizer()` - Numeric column to discretized column +- `ft_count_vectorizer()` - Extracts a vocabulary from document +- `ft_discrete_cosine_transform()` - 1D discrete cosine transform of a real vector +- `ft_elementwise_product()` - Element- wise product between 2 cols +- `ft_hashing_tf()` - Maps a sequence of terms to their term frequencies using the hashing trick. +- `ft_idf()` - Compute the Inverse Document Frequency (IDF) given a collection of documents. +- `ft_imputer()` - Imputation estimator for completing missing values, uses the mean or the median of the columns. +- `ft_index_to_string()` - Index labels back to label as strings +- `ft_interaction()` - Takes in Double and Vector columns and outputs a flattened vector of their feature interactions. +- `ft_max_abs_scaler()` - Rescale each feature individually to range \[-1, 1\] (Supported in Databricks Connect v2) +- `ft_min_max_scaler()` - Rescale each feature to a common range \[min, max\] linearly +- `ft_ngram()` - Converts the input array of strings into an array of n-grams +- `ft_bucketed_random_projection_lsh()` +- `ft_minhash_lsh()` - Locality Sensitive Hashing functions for Euclidean distance and Jaccard distance (MinHash) +- `ft_normalizer()` - Normalize a vector to have unit norm using the given p-norm +- `ft_one_hot_encoder()` - Continuous to binary vectors +- `ft_pca()` - Project vectors to a lower dimensional space of top k principal components +- `ft_quantile_discretizer()` - Continuous to binned categorical values. +- `ft_regex_tokenizer()` - Extracts tokens either by using the provided regex pattern to split the text +- `ft_robust_scaler()` - Removes the median and scales according to standard scale +- `ft_standard_scaler()` - Removes the mean and scaling to unit variance using column summary statistics (Supported in Databricks Connect v2) +- `ft_stop_words_remover()` - Filters out stop words from input +- `ft_string_indexer()` - Column of labels into a column of label indices. +- `ft_tokenizer()` - Converts to lowercase and then splits it by white spaces +- `ft_vector_assembler()` - Combine vectors into single row-vector +- `ft_vector_indexer()` - Indexing categorical feature columns in a dataset of Vector +- `ft_vector_slicer()` - Takes a feature vector and outputs a new feature vector with a subarray of the original features +- `ft_word2vec()` - Word2Vec transforms a word into a code + + + +# Visualize + +## dplyr + ggplot2 + +Supported in Databricks Connect v2 + +```{r} +copy_to(sc, mtcars) %>% + group_by(cyl) %>% + summarise(mpg_m = mean(mpg)) %>% # Summarize in Spark + collect() %>% # Collect results in R + ggplot() + + geom_col(aes(cyl, mpg_m)) # Create plot +``` + +# Modeling + +## Regression + +- `ml_linear_regression()` - Linear regression. +- `ml_aft_survival_regression()` - Parametric survival regression model named accelerated failure time (AFT) model. +- `ml_generalized_linear_regression()` - GLM. +- `ml_isotonic_regression()` - Uses parallelized pool adjacent violators algorithm. +- `ml_random_forest_regressor()` - Regression using random forests. + +## Classification + +- `ml_linear_svc()` - Classification using linear support vector machines. +- `ml_logistic_regression()` - Logistic regression. (Supported in Databricks Connect v2) +- `ml_multilayer_perceptron_classifier()` - Based on the Multilayer Perceptron. +- `ml_naive_bayes()` - It supports Multinomial NB which can handle finitely supported discrete data. +- `ml_one_vs_rest()` - Reduction of Multiclass, performs reduction using one against all strategy. + +## Tree + +- `ml_decision_tree_classifier()`, `ml_decision_tree()`, \`ml_decision_tree_regressor(). - Classification and regression using decision trees. +- `ml_gbt_classifier()`, `ml_gradient_boosted_trees()`, `ml_gbt_regressor()` - Binary classification and regression using gradient boosted trees. +- `ml_random_forest_classifier()` - Classification and regression using random forests. +- `ml_feature_importances()`, `ml_tree_feature_importance()` - Feature Importance for Tree Models. + +## Clustering + +- `ml_bisecting_kmeans()` - A bisecting k-means algorithm based on the paper. +- `ml_lda()`, `ml_describe_topics()`, `ml_log_likelihood()`, `ml_log_perplexity()`, `ml_topics_matrix()` - LDA topic model designed for text documents. +- `ml_gaussian_mixture()` - Expectation maximization for multivariate Gaussian Mixture Models (GMMs). +- `ml_kmeans()`, `ml_compute_cost()`, `ml_compute_silhouette_measure()` - Clustering with support for k-means. +- `ml_power_iteration()` - For clustering vertices of a graph given pairwise similarities as edge properties. + +## Recommendation + +- `ml_als()`, `ml_recommend()` - Recommendation using Alternating Least Squares matrix factorization. + +## Evaluation + +- `ml_clustering_evaluator()` - Evaluator for clustering. +- `ml_evaluate()` - Compute performance metrics. +- `ml_binary_classification_evaluator()`, `ml_binary_classification_eval()`, `ml_classification_eval()` - A set of functions to calculate performance metrics for prediction models. + +## Frequent pattern + +- `ml_fpgrowth()`, `ml_association_rules()`, `ml_freq_itemsets()` - A parallel FP-growth algorithm to mine frequent itemsets. +- `ml_freq_seq_patterns()`, `ml_prefixspan()` - PrefixSpan algorithm for mining frequent itemsets. + +## Stats + +- `ml_summary()` - Extracts a metric from the summary object of a Spark ML model. +- `ml_corr()` - Compute correlation matrix. + +## Feature + +- `ml_chisquare_test(x,features,label)` - Pearson's independence test for every feature against the label. +- `ml_default_stop_words()` - Loads the default stop words for the given language. + +## Utilities + +- `ml_call_constructor()` - Identifies the associated sparklyr ML constructor for the JVM. +- `ml_model_data()` - Extracts data associated with a Spark ML model. +- `ml_standardize_formula()` - Generates a formula string from user inputs. +- `ml_uid()` - Extracts the UID of an ML object. + +# ML pipelines + +Easily create a formal Spark Pipeline models using R. +Save the Pipeline in native Sacala. +It will have **no dependencies** on R. + +## Initialize and train + +Supported in Databricks Connect v2 + +- `ml_pipeline()` - Initializes a new Spark Pipeline. +- `ml_fit()` - Trains the model, outputs a Spark Pipeline Model. + +## Save and retrieve + +Supported in Databricks Connect v2 + +- `ml_save()` - Saves into a format that can be read by Scala and PySpark. +- `ml_read()` - Reads Spark object into sparklyr. + +![](images/sparklyr-save-retrieve.png){fig-alt="ml_pipeline() to ft_dplyr_transformer to ft_bucketizer() to ml_linear_regression() to ml_fit() to ml_save()." fig-align="center" width="501"} + +[spark.posit.co/guides/pipelines](https://spark.posit.co/guides/pipelines) + +# Distributed R + +Supported in Databricks Connect v2 + +Run arbitrary R code at scale inside your cluster with spark_apply(). +Useful when there you need functionality only available in R, and to solve 'embarrassingly parallel problems'. + +```{r} +spark_apply( + x, f, + columns = NULL, memory = TRUE, group_by = NULL, + name = NULL, barrier = NULL, fetch_result_as_sdf = TRUE + ) +``` + +```{r} +copy_to(sc, mtcars) %>% + spark_apply( + nrow, # R only function + group_by = "am", + columns = "am double, x long" + ) +``` + +------------------------------------------------------------------------ + +CC BY SA Posit Software, PBC • [info\@posit.co](mailto:info@posit.co) • [posit.co](https://posit.co) + +Learn more at [spark.posit.co](https://spark.posit.co/) and [therinspark.com](https://therinspark.com/). + +Updated: `r format(Sys.Date(), "%Y-%m")`. + +```{r} +#| output: true +#| eval: true + +packageVersion("sparklyr") +``` + +------------------------------------------------------------------------