> For the complete documentation index, see [llms.txt](https://docs.redivis.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.redivis.com/api/client-libraries/redivis-r/reference/readstream.md).

# ReadStream

## *class* <mark style="color:purple;">ReadStream</mark>

ReadStreams are an advanced concept that can be used for processing large tabular data in parallel. For example, a table can be broken into multiple ReadStreams for processing, where each stream can be read simultaneous in a separate thread / process.

## Constructors

<table data-header-hidden><thead><tr><th width="334.4296875">Method</th><th>Description</th></tr></thead><tbody><tr><td><a href="/api/client-libraries/redivis-r/reference/upload/uploadusdto_.md"><strong><code>Upload$to_read_streams</code></strong></a>(target_count)</td><td>Create parallel read streams to read an upload.</td></tr><tr><td><a href="/api/client-libraries/redivis-r/reference/query/queryusdto_.md"><strong><code>Query$to_read_streams</code></strong></a>(target_count)</td><td>Create parallel read streams to read query results.</td></tr><tr><td><a href="/api/client-libraries/redivis-r/reference/table/tableusdto_read_streams.md"><strong><code>Table$to_read_streams</code></strong></a>(target_count)</td><td>Create parallel read streams to read a table.</td></tr></tbody></table>

## Examples

```r
library(redivis)
library(arrow)
library(parallel)

table <- redivis$table(
  "demo.cms_2014_medicare_data:349j.physicians_and_other_supplier:kn00"
)

streams <- table$to_read_streams(
  variables = list("average_submitted_chrg_amt"),
  target_count = 4L
)

process_stream <- function(stream) {
  reader <- stream$to_arrow_batch_reader()
  on.exit(reader$close())
  total <- 0
  count <- 0L

  while (TRUE) {
    batch <- reader$read_next_batch()
    if (is.null(batch)) break

    column <- as.vector(batch[["average_submitted_chrg_amt"]])
    total <- total + as.numeric(sum(column, na.rm = TRUE))
    count <- count + length(column)
  }

  list(total = total, count = count)
}

# Process streams in parallel
future::plan(future::multisession, workers = length(streams))
results <- furrr::future_map(streams, process_stream)

grand_total <- sum(vapply(results, `[[`, numeric(1), "total"))
grand_count <- sum(vapply(results, `[[`, integer(1), "count"))

average <- if (grand_count > 0) grand_total / grand_count else 0

cat(sprintf("Average submitted charge amount: $%.2f\n", average))
cat(sprintf(
  "Computed across %d rows using %d parallel streams\n",
  grand_count,
  length(streams)
))
```

## Attributes

| **`properties`** | A dict containing the attributes `id` and `estimatedRows`. |
| ---------------- | ---------------------------------------------------------- |

## Methods

<table data-header-hidden><thead><tr><th width="441"></th><th></th></tr></thead><tbody><tr><td><a href="/api/client-libraries/redivis-r/reference/readstream/readstreamusdto_.md"><strong><code>ReadStream$to_*</code></strong></a>([max_results, *, ...])</td><td>Various methods to read query results. Mirrors the various <a href="/api/client-libraries/redivis-r/reference/table.md">Table$to_*</a> methods (e.g., <code>ReadStream$to_tibble()</code></td></tr></tbody></table>


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