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Getting started with icebergr

Every example here runs offline against a table built on your own machine. No catalog server, no network, no credentials.

library(icebergr)

What this package is for

Iceberg is the open table format that Snowflake, Databricks, BigQuery, AWS and Dremio have all standardised on. Apache maintains clients for Java, Python, Rust and Go — but not R, which has been able to read Iceberg tables only by routing through DuckDB. That rules out writes, snapshot management, schema access and catalog integration.

icebergr binds iceberg-rust directly. Arrow is the interchange layer, so scan results arrive in R without a serialisation round trip.

A table to work with

icebergr_example_table() builds a real Iceberg table in a temporary warehouse directory: two appends, so there is history to travel through and more than one data file for a filter to prune.

tbl <- icebergr_example_table(rows = 200)
tbl
#> <icebergr_table>
#>   table:    db.events
#>   location: /project/rcc/youzhi/.cache/tmp/RtmpzqaPBY/icebergr-warehouse6c2045a4bbc33/db/events
#>   format:   v2
#>   snapshot: 6935205264320674142
#>   columns:  5
#>     id <int>
#>     event <string>
#>     amount <double>
#>     day <date>
#>     recorded_at <timestamptz>

It is generated rather than shipped because Iceberg records absolute paths in its metadata and its Avro manifests — a table built on one machine does not resolve on another.

Inspecting a table

icebergr_schema(tbl)
#> # A tibble: 5 × 5
#>   field_id name        type        required doc  
#>      <int> <chr>       <chr>       <lgl>    <chr>
#> 1        1 id          int         FALSE    <NA> 
#> 2        2 event       string      FALSE    <NA> 
#> 3        3 amount      double      FALSE    <NA> 
#> 4        4 day         date        FALSE    <NA> 
#> 5        5 recorded_at timestamptz FALSE    <NA>

type is the Iceberg type, not the R type. The mapping to R happens on read.

An unpartitioned table returns zero partition fields:

icebergr_partitions(tbl)
#> # A tibble: 0 × 6
#> # ℹ 6 variables: spec_id <int>, field_id <int>, name <chr>, transform <chr>,
#> #   source_id <int>, source_name <chr>

Reading

icebergr_scan() describes a read; icebergr_collect() performs it.

icebergr_collect(icebergr_scan(tbl, limit = 5))
#> # A tibble: 5 × 5
#>      id event    amount day        recorded_at        
#>   <int> <chr>     <dbl> <date>     <dttm>             
#> 1     1 click      0.5  2024-01-01 2024-01-01 00:00:00
#> 2     2 view       1.75 2024-01-02 2024-01-01 01:00:00
#> 3     3 purchase   3.01 2024-01-03 2024-01-01 02:00:00
#> 4     4 scroll     4.26 2024-01-04 2024-01-01 03:00:00
#> 5     5 click      5.52 2024-01-05 2024-01-01 04:00:00

Scanning the whole table is common enough to have a shorthand:

nrow(icebergr_collect(tbl))
#> [1] 400

Pushdown

filter and select are pushed into scan planning. This is the entire performance argument for Iceberg over reading raw Parquet: manifests carry per-file statistics, so whole files and row groups are eliminated before any bytes are read.

icebergr_collect(
  icebergr_scan(
    tbl,
    filter = id > 1000 & amount > 900,
    select = c("id", "event", "amount")
  )
)
#> # A tibble: 40 × 3
#>       id event    amount
#>    <int> <chr>     <dbl>
#>  1  1161 purchase   902.
#>  2  1162 refund     905.
#>  3  1163 purchase   907.
#>  4  1164 refund     910.
#>  5  1165 purchase   912.
#>  6  1166 refund     915.
#>  7  1167 purchase   917.
#>  8  1168 refund     920.
#>  9  1169 purchase   922.
#> 10  1170 refund     925.
#> # ℹ 30 more rows

Filters may use ==, !=, <, <=, >, >=, &, |, !, %in%, is.na(), is.nan() and startsWith() – the last only against a string column, since Iceberg defines a prefix comparison for no other type. A bare name is read as a column when the table has one of that name, and otherwise evaluated in the calling environment:

# The second append holds ids 1001 to 1200, so this keeps the last fifty.
threshold <- 1150
icebergr_collect(icebergr_scan(tbl, filter = id > threshold, select = "id"))
#> # A tibble: 50 × 1
#>       id
#>    <int>
#>  1  1151
#>  2  1152
#>  3  1153
#>  4  1154
#>  5  1155
#>  6  1156
#>  7  1157
#>  8  1158
#>  9  1159
#> 10  1160
#> # ℹ 40 more rows

Anything more elaborate is refused rather than quietly ignored, so you always know whether a filter was pushed down:

icebergr_collect(icebergr_scan(tbl, filter = sqrt(amount) > 10))
#> Error in `icebergr_scan()`:
#> ! Cannot push this filter down to Iceberg: neither side of `sqrt(amount) > 10` names a column of the table. Columns are: id, event, amount, day, recorded_at.
#> ℹ Supported: ==, !=, <, <=, >, >=, &, |, !, %in%, is.na(), is.nan() and startsWith().
#> ℹ Anything else can be applied in R after icebergr_collect().

Verifying that pushdown happened

Comparing results proves nothing: a filter applied in R afterwards gives the same rows. What distinguishes pushdown is how much was planned to be read.

icebergr_scan_plan(icebergr_scan(tbl))[, c("record_count", "file_size_in_bytes")]
#> # A tibble: 2 × 2
#>   record_count file_size_in_bytes
#>          <dbl>              <dbl>
#> 1          200               4901
#> 2          200               4998
icebergr_scan_plan(icebergr_scan(tbl, filter = id > 1000))[, c("record_count")]
#> # A tibble: 1 × 1
#>   record_count
#>          <dbl>
#> 1          200

Fewer files, and fewer records, than the table holds.

One caveat about limit

limit is not pushdown. iceberg-rust has no row limit in its scan API, so the same files are planned and rows are counted as batches arrive. It bounds how much is decoded, not how much is planned. print() says so:

icebergr_scan(tbl, filter = id > 1000, limit = 10)
#> <icebergr_scan>
#>   table:    db.events
#>   select:   <all columns>
#>   filter:   id > 1000 (pushed down)
#>   limit:    10 (applied after the scan, not pushed down)
#>   Use icebergr_collect() to read it.

Time travel

Every write creates a snapshot.

history <- icebergr_snapshots(tbl)
history[, c("snapshot_id", "operation", "added_records", "total_records")]
#> # A tibble: 2 × 4
#>   snapshot_id         operation added_records total_records
#>   <chr>               <chr>             <dbl>         <dbl>
#> 1 2210251406760931063 append              200           200
#> 2 6935205264320674142 append              200           400

Snapshot ids are character, not numeric. Iceberg assigns them as random 64-bit integers, and an R numeric carries only 53 bits, so passing one through a double would silently select the wrong snapshot.

Read an earlier state by id:

nrow(icebergr_collect(icebergr_scan(tbl, snapshot_id = history$snapshot_id[[1]])))
#> [1] 200

Or by time, which is resolved against the history to the snapshot that was current at that moment:

nrow(icebergr_collect(icebergr_scan(tbl, as_of = history$timestamp[[1]])))
#> [1] 200

Iceberg records a schema per snapshot, so filter and select are resolved against the schema of the snapshot actually being read rather than the current one. A column another engine has since renamed or dropped is therefore still nameable as of the snapshot that had it, and icebergr_schema() will show you what those columns were:

icebergr_schema(tbl, snapshot_id = history$snapshot_id[[1]])
#> # A tibble: 5 × 5
#>   field_id name        type        required doc  
#>      <int> <chr>       <chr>       <lgl>    <chr>
#> 1        1 id          int         FALSE    <NA> 
#> 2        2 event       string      FALSE    <NA> 
#> 3        3 amount      double      FALSE    <NA> 
#> 4        4 day         date        FALSE    <NA> 
#> 5        5 recorded_at timestamptz FALSE    <NA>

Writing

Writes are append-only. Nothing already in the table is rewritten or removed.

new_rows <- data.frame(
  id = c(9001L, 9002L),
  event = c("purchase", "refund"),
  amount = c(42.5, -12.25),
  day = as.Date(c("2024-07-01", "2024-07-02")),
  recorded_at = as.POSIXct(c("2024-07-01 09:00:00", "2024-07-02 10:30:00"), tz = "UTC")
)

tbl <- icebergr_append(tbl, new_rows)
nrow(icebergr_collect(tbl))
#> [1] 402

icebergr_append() returns an updated handle rather than mutating the old one, so reassign it. The old handle still reads the older snapshot, which is occasionally useful and never surprising.

Columns are matched by name, not position, so order does not matter. A column the table does not have is an error rather than a silent drop:

icebergr_append(tbl, transform(new_rows, unexpected = 1))
#> Error in rs_table_append(tbl = tbl$ptr, stream_addr = holder$addr, compression = compression, : the data has 1 column(s) that the table does not: unexpected.
#> Table columns are: amount, day, event, id, recorded_at.

Creating a table

A data frame is enough to define a schema. Only the column names and types are used; no rows are written.

warehouse <- tempfile("warehouse")
dir.create(warehouse)

catalog <- icebergr_catalog("memory", warehouse = warehouse)
icebergr_create_namespace(catalog, "analytics")

measurements <- data.frame(
  sensor = character(),
  reading = double(),
  taken_at = as.POSIXct(character(), tz = "UTC")
)

sensors <- icebergr_create_table(catalog, "analytics.measurements", measurements)
icebergr_schema(sensors)
#> # A tibble: 3 × 5
#>   field_id name     type        required doc  
#>      <int> <chr>    <chr>       <lgl>    <chr>
#> 1        1 sensor   string      FALSE    <NA> 
#> 2        2 reading  double      FALSE    <NA> 
#> 3        3 taken_at timestamptz FALSE    <NA>

Type fidelity

Most R types survive unchanged. The exception worth knowing is factor: Iceberg has no dictionary type, so levels cannot be carried and values come back as character.

types <- data.frame(
  i = 1L,
  d = 1.5,
  s = "text",
  b = TRUE,
  day = as.Date("2024-01-01"),
  ts = as.POSIXct("2024-01-01 12:00:00", tz = "UTC"),
  f = factor("a", levels = c("a", "b"))
)

type_tbl <- icebergr_create_table(catalog, "analytics.types", types)
type_tbl <- icebergr_append(type_tbl, types)

vapply(icebergr_collect(type_tbl), function(x) class(x)[[1]], character(1))
#>           i           d           s           b         day          ts 
#>   "integer"   "numeric" "character"   "logical"      "Date"   "POSIXct" 
#>           f 
#> "character"

Knowing what is not supported

icebergr 0.1.0 is deliberately narrow. Rather than discovering a gap at runtime, ask:

support <- icebergr_spec_support()
support$spec_versions
#> [1] 1 2
features <- support$features
unsupported <- !is.na(features$supported) & !features$supported
features[unsupported, c("feature", "reason")]
#> # A tibble: 16 × 2
#>    feature                       reason                                         
#>    <chr>                         <chr>                                          
#>  1 AWS Glue catalog              Requires the optional 'glue' Cargo feature     
#>  2 Object storage (S3)           Requires the optional 's3' Cargo feature       
#>  3 Nested field pushdown         iceberg-rust cannot plan a scan filtered or pr…
#>  4 Table properties (write)      Needs an update_properties transaction; out of…
#>  5 Hadoop/filesystem catalog     Not implemented in iceberg-rust; use type = 'm…
#>  6 Row limit pushdown            iceberg-rust has no row limit in its scan API;…
#>  7 Row-level deletes (write)     iceberg-rust 0.10.0 can write an equality dele…
#>  8 MERGE / upsert                Needs row-level deletes plus an overwrite, nei…
#>  9 Overwrite writes              iceberg-rust 0.10.0 has no overwrite or rewrit…
#> 10 Schema evolution              Out of scope for icebergr 0.1.0                
#> 11 Partitioned table creation    Out of scope for icebergr 0.1.0                
#> 12 Append to a partitioned table An append would have to compute a partition va…
#> 13 Partition evolution           Out of scope for icebergr 0.1.0                
#> 14 Compaction / maintenance      Compaction needs a rewrite action iceberg-rust…
#> 15 dbplyr lazy verbs             Out of scope for icebergr 0.1.0                
#> 16 Table encryption              Not exposed in icebergr 0.1.0

Some of those are absent from iceberg-rust itself, not just from this package; the reason column says which.

Where next

vignette("catalog-configuration") covers connecting to REST catalogs, AWS Glue and object storage, and how credentials are handled.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.