tree: 696073907e729b16871e1c27906b176f5315c8dd

README.md

Apache Spark Connect Client for Rust

This project houses the experimental client for Spark Connect for Apache Spark written in Rust

Current State of the Project

Currently, the Spark Connect client for Rust is highly experimental and should not be used in any production setting. This is currently a “proof of concept” to identify the methods of interacting with Spark cluster from rust.

The spark-connect-rs aims to provide an entrypoint to Spark Connect, and provide similar DataFrame API interactions.

Project Layout

├── core       <- core implementation in Rust
│   └─ spark   <- git submodule for apache/spark
├── rust       <- shim for 'spark-connect-rs' from core

Future state would be to have additional bindings for other languages along side the top level rust folder.

Getting Started

This section explains how run Spark Connect Rust locally starting from 0.

Step 1: Install rust via rustup: https://www.rust-lang.org/tools/install

Step 2: Ensure you have a cmake and protobuf installed on your machine

Step 3: Run the following commands to clone the repo

git clone https://github.com/sjrusso8/spark-connect-rs.git
git submodule update --init --recursive

cargo build

Step 4: Setup the Spark Driver on localhost either by downloading spark or with docker.

With local spark:

Download Spark distribution (3.4.0+), unzip the package.
Start the Spark Connect server with the following command (make sure to use a package version that matches your Spark distribution):

sbin/start-connect-server.sh --packages org.apache.spark:spark-connect_2.12:3.4.0

With docker:

Start the Spark Connect server by leveraging the created docker-compose.yml in this repo. This will start a Spark Connect Server running on port 15002

docker compose up --build -d

Step 5: Run an example from the repo under /examples

Features

The following section outlines some of the larger functionality that are not yet working with this Spark Connect implementation.

TLS authentication & Databricks compatability via the feature flag feature = 'tls'
StreamingQueryManager
UDFs or any type of functionality that takes a closure (foreach, foreachBatch, etc.)

SparkSession

Spark Session type object and its implemented traits

SparkSession	API	Comment
active
appName
catalog		Partial. Only Get/List traits are implemented
createDataFrame		Partial. Only works for `RecordBatch`
range
read
readStream		Creates a `DataStreamReader` object
sql
stop
streams		Stream Manager is not yet implemented
table
version
addArtifact(s)
interruptAll
interruptTag
interruptOperation
addTag
removeTag
getTags
clearTags

DataFrame

Spark DataFrame type object and its implemented traits.

DataFrame	API	Comment
agg
alias
approxQuantile
cache
checkpoint
coalesce
colRegex
collect
columns
corr
count
cov
createGlobalTempView
createOrReplaceGlobalTempView
createOrReplaceTempView
createTempView
crossJoin
crosstab
cube
describe
distinct
drop
dropDuplicates
dropDuplicatesWithinWatermark		Windowing functions are currently in progress
drop_duplicates
dropna
dtypes
exceptAll
explain
fillna
filter
first
foreach
foreachPartition
freqItems
groupBy
head
hint
inputFiles
intersect
intersectAll
isEmpty
isLocal
isStreaming
join
limit
localCheckpoint
mapInPandas		TBD on this exact implementation
mapInArrow		TBD on this exact implementation
melt
na
observe
offset
orderBy
persist
printSchema
randomSplit
registerTempTable
repartition
repartitionByRange
replace
rollup
sameSemantics
sample
sampleBy
schema
select
selectExpr
semanticHash
show
sort
sortWithinPartitions
sparkSession
stat
storageLevel
subtract
summary
tail
take
to
toDF
toJSON
toLocalIterator
toPandas		TBD on this exact implementation. Might be toPolars
transform
union
unionAll
unionByName
unpersist
unpivot
where		use `filter` instead, `where` is a keyword for rust
withColumn
withColumns
withColumnRenamed
withColumnsRenamed
withMetadata
withWatermark
write
writeStream
writeTo

DataFrameWriter

Spark Connect should respect the format as long as your cluster supports the specified type and has the required jars

DataFrameWriter	API	Comment
format
option
options
mode
bucketBy
sortBy
partitionBy
save
saveAsTable
insertInto

DataStreamWriter

Start a streaming job and return a StreamingQuery object to handle the stream operations.

DataStreamWriter	API	Comment
format
foreach
foreachBatch
option
options
outputMode		Uses an Enum for `OutputMode`
partitionBy
queryName
trigger		Uses an Enum for `TriggerMode`
start
toTable

StreamingQuery

A handle to a query that is executing continuously in the background as new data arrives.

StreamingQuery	API	Comment
awaitTermination
exception
explain
processAllAvailable
stop
id
isActive
lastProgress
name
recentProgress
runId
status

Column

Spark Column type object and its implemented traits

Column	API	Comment
alias
asc
asc_nulls_first
asc_nulls_last
astype
between
cast
contains
desc
desc_nulls_first
desc_nulls_last
dropFields
endswith
eqNullSafe
getField		This is depreciated but will need to be implemented
getItem		This is depreciated but will need to be implemented
ilike
isNotNull
isNull
isin
like
name
otherwise
over		Refer to Window for creating window specifications
rlike
startswith
substr
when
withField
eq `==`		Rust does not like when you try to overload `==` and return something other than a `bool`. Currently implemented column equality like `col('name').eq(col('id'))`. Not the best, but it works for now
addition `+`
subtration `-`
multiplication `*`
division `/`
OR `\|`
AND `&`
XOR `^`
Negate `~`

Functions

Only a few of the functions are covered by unit tests.

Functions	API	Comment
abs
acos
acosh
add_months
aggregate
approxCountDistinct
approx_count_distinct
array
array_append
array_compact
array_contains
array_distinct
array_except
array_insert
array_intersect
array_join
array_max
array_min
array_position
array_remove
array_repeat
array_sort
array_union
arrays_overlap
arrays_zip
asc
asc_nulls_first
asc_nulls_last
ascii
asin
asinh
assert_true
atan
atan2
atanh
avg
base64
bin
bit_length
bitwiseNOT
bitwise_not
broadcast
bround
bucket
call_udf
cbrt
ceil
coalesce
col
collect_list
collect_set
column
concat
concat_ws
conv
corr
cos
cosh
cot
count
countDistinct
count_distinct
covar_pop
covar_samp
crc32
create_map
csc
cume_dist
current_date
current_timestamp
date_add
date_format
date_sub
date_trunc
datediff
dayofmonth
dayofweek
dayofyear
days
decode
degrees
dense_rank
desc
desc_nulls_first
desc_nulls_last
element_at
encode
exists
exp
explode
explode_outer
expm1
expr
factorial
filter
first
flatten
floor
forall
format_number
format_string
from_csv
from_json
from_unixtime
from_utc_timestamp
functools
get
get_active_spark_context
get_json_object
greatest
grouping
grouping_id
has_numpy
hash
hex
hour
hours
hypot
initcap
inline
inline_outer
input_file_name
inspect
instr
isnan
isnull
json_tuple
kurtosis
lag
last
last_day
lead
least
length
levenshtein
lit
localtimestamp
locate
log
log10
log1p
log2
lower
lpad
ltrim
make_date
map_concat
map_contains_key
map_entries
map_filter
map_from_arrays
map_from_entries
map_keys
map_values
map_zip_with
max
max_by
md5
mean
median
min
min_by
minute
mode
monotonically_increasing_id
month
months
months_between
nanvl
next_day
np
nth_value
ntile
octet_length
overlay
overload
pandas_udf
percent_rank
percentile_approx
pmod
posexplode
posexplode_outer
pow
product
quarter
radians
raise_error
rand
randn
rank
regexp_extract
regexp_replace
repeat
reverse
rint
round
row_number
rpad
rtrim
schema_of_csv
schema_of_json
sec
second
sentences
sequence
session_window
sha1
sha2
shiftLeft
shiftRight
shiftRightUnsigned
shiftleft
shiftright
shiftrightunsigned
shuffle
signum
sin
sinh
size
skewness
slice
sort_array
soundex
spark_partition_id
split
sqrt
stddev
stddev_pop
stddev_samp
struct
substring
substring_index
sum
sumDistinct
sum_distinct
sys
tan
tanh
timestamp_seconds
toDegrees
toRadians
to_csv
to_date
to_json
to_str
to_timestamp
to_utc_timestamp
transform
transform_keys
transform_values
translate
trim
trunc
try_remote_functions
udf
unbase64
unhex
unix_timestamp
unwrap_udt
upper
var_pop
var_samp
variance
warnings
weekofyear
when
window
window_time
xxhash64
year
years
zip_with

Schema

Spark schema objects have not yet been translated into rust objects.

Literal Types

Create Spark literal types from these rust types. E.g. lit(1_i64) would be a LongType() in the schema.

An array can be made like lit([1_i16,2_i16,3_i16]) would result in an ArrayType(Short) since all the values of the slice can be translated into literal type.

Spark Literal Type	Rust Type	Status
Null
Binary	`&[u8]`
Boolean	`bool`
Byte
Short	`i16`
Integer	`i32`
Long	`i64`
Float	`f32`
Double	`f64`
Decimal
String	`&str` / `String`
Date	`chrono::NaiveDate`
Timestamp	`chrono::DateTime<Tz>`
TimestampNtz	`chrono::NaiveDateTime`
CalendarInterval
YearMonthInterval
DayTimeInterval
Array	`slice` / `Vec`
Map	Create with the function `create_map`
Struct	Create with the function `struct_col` or `named_struct`

Window & WindowSpec

For ease of use it's recommended to use Window to create the WindowSpec.

Window	API	Comment
currentRow
orderBy
partitionBy
rangeBetween
rowsBetween
unboundedFollowing
unboundedPreceding
WindowSpec.orderBy
WindowSpec.partitionBy
WindowSpec.rangeBetween
WindowSpec.rowsBetween