NEWS.md
EXPLAIN (TYPE validate) (#225) thanks to @tyner for raising issuenoctua_options (#226) thanks to @tyner for raising issueSelectedEngineVersion in update_work_group (#224) thanks to @tyner for raising issuedbExistsTable to catch update AWS error message.dbplyr 2.3.3.9000+
AWS_ROLE_ARN. This caused confusing when connecting through web identity (RAthena # 177)dbplyr::in_catalog when working with dplyr::tbl (RAthena # 178)dbClearResult (RAthena # 168). Thanks to @juhoautio for the request.paws parameters (RAthena # 169)endpoint_override parameter allow default endpoints for each service to be overridden accordingly (RAthena # 169). Thanks to @aoyh for the request and checking the package in development.noctua_options to change 1 parameter at a time without affecting other pre-configured settingsretry_quiet parameter in noctua_options function.dbplyr 2.0.0 backend API.dplyr to benefit from AWS Athena unload methods (#174).AWS Athena UNLOAD (#160). This is to take advantage of read/write speed parquet has to offer.import awswrangler as wr
import getpass
bucket = getpass.getpass()
path = f"s3://{bucket}/data/"
if "awswrangler_test" not in wr.catalog.databases().values:
wr.catalog.create_database("awswrangler_test")
cols = ["id", "dt", "element", "value", "m_flag", "q_flag", "s_flag", "obs_time"]
df = wr.s3.read_csv(
path="s3://noaa-ghcn-pds/csv/189",
names=cols,
parse_dates=["dt", "obs_time"]) # Read 10 files from the 1890 decade (~1GB)
wr.s3.to_parquet(
df=df,
path=path,
dataset=True,
mode="overwrite",
database="awswrangler_test",
table="noaa"
);
wr.catalog.table(database="awswrangler_test", table="noaa")
library(DBI)
con <- dbConnect(noctua::athena())
# Query ran using CSV output
system.time({
df = dbGetQuery(con, "SELECT * FROM awswrangler_test.noaa")
})
# Info: (Data scanned: 80.88 MB)
# user system elapsed
# 57.004 8.430 160.567
noctua::noctua_options(cache_size = 1)
# Query ran using UNLOAD Parquet output
system.time({
df = dbGetQuery(con, "SELECT * FROM awswrangler_test.noaa", unload = T)
})
# Info: (Data scanned: 80.88 MB)
# user system elapsed
# 21.622 2.350 39.232
# Query ran using cache
system.time({
df = dbGetQuery(con, "SELECT * FROM awswrangler_test.noaa", unload = T)
})
# Info: (Data scanned: 80.88 MB)
# user system elapsed
# 13.738 1.886 11.029 AWS Athena timestamp with time zone data type.list when converting data to AWS Athena SQL format.
library(data.table)
library(DBI)
x = 5
dt = data.table(
var1 = sample(LETTERS, size = x, T),
var2 = rep(list(list("var3"= 1:3, "var4" = list("var5"= letters[1:5]))), x)
)
con <- dbConnect(noctua::athena())
#> Version: 2.2.0
sqlData(con, dt)
# Registered S3 method overwritten by 'jsonify':
# method from
# print.json jsonlite
# Info: Special characters "\t" has been converted to " " to help with Athena reading file format tsv
# var1 var2
# 1: 1 {"var3":[1,2,3],"var4":{"var5":["a","b","c","d","e"]}}
# 2: 2 {"var3":[1,2,3],"var4":{"var5":["a","b","c","d","e"]}}
# 3: 3 {"var3":[1,2,3],"var4":{"var5":["a","b","c","d","e"]}}
# 4: 4 {"var3":[1,2,3],"var4":{"var5":["a","b","c","d","e"]}}
# 5: 5 {"var3":[1,2,3],"var4":{"var5":["a","b","c","d","e"]}}
#> Version: 2.1.0
sqlData(con, dt)
# Info: Special characters "\t" has been converted to " " to help with Athena reading file format tsv
# var1 var2
# 1: 1 1:3|list(var5 = c("a", "b", "c", "d", "e"))
# 2: 2 1:3|list(var5 = c("a", "b", "c", "d", "e"))
# 3: 3 1:3|list(var5 = c("a", "b", "c", "d", "e"))
# 4: 4 1:3|list(var5 = c("a", "b", "c", "d", "e"))
# 5: 5 1:3|list(var5 = c("a", "b", "c", "d", "e"))v-2.2.0 now converts lists into json lines format so that AWS Athena can parse with sql array/mapping/json functions. Small down side a s3 method conflict occurs when jsonify is called to convert lists into json lines. jsonify was choose in favor to jsonlite due to the performance improvements (#156).
dbIsValid wrongly stated connection is valid for result class when connection class was disconnected.sql_translate_env.paste broke with latest version of dbplyr. New method is compatible with dbplyr>=1.4.3 (#149).sql_translate_env: add support for stringr/lubridate style functions, similar to Postgres backend.write_bin now doesn’t chunk writeBin if R version is greater than 4.0.0 https://github.com/HenrikBengtsson/Wishlist-for-R/issues/97 (#149)dbConnect add timezone parameter so that time zone between R and AWS Athena is consistent.AthenaConnection class: ptr and info slots changed from list to environment with in AthenaConnect class. Allows class to be updated by reference. Simplifies notation when viewing class from RStudio environment tab.AthenaResult class: info slot changed from list to environment. Allows class to be updated by reference.By utilising environments for AthenaConnection and AthenaResult, all AthenaResult classes created from AthenaConnection will point to the same ptr and info environments for it’s connection. Previously ptr and info would make a copy. This means if it was modified it would not affect the child or parent class for example:
# Old Method
library(DBI)
con <- dbConnect(noctua::athena(),
rstudio_conn_tab = F)
res <- dbExecute(con, "select 'helloworld'")
# modifying parent class to influence child
con@info$made_up <- "helloworld"
# nothing happened
res@connection@info$made_up
# > NULL
# modifying child class to influence parent
res@connection@info$made_up <- "oh no!"
# nothing happened
con@info$made_up
# > "helloworld"
# New Method
library(DBI)
con <- dbConnect(noctua::athena(),
rstudio_conn_tab = F)
res <- dbExecute(con, "select 'helloworld'")
# modifying parent class to influence child
con@info$made_up <- "helloworld"
# picked up change
res@connection@info$made_up
# > "helloworld"
# modifying child class to influence parent
res@connection@info$made_up <- "oh no!"
# picked up change
con@info$made_up
# > "oh no!"AWS Athena data types [array, row, map, json, binary, ipaddress] (#135). Conversion types can be changed through dbConnect and noctua_options.
library(DBI)
library(noctua)
# default conversion methods
con <- dbConnect(noctua::athena())
# change json conversion method
noctua_options(json = "character")
noctua:::athena_option_env$json
# [1] "character"
# change json conversion to custom method
noctua_options(json = jsonify::from_json)
noctua:::athena_option_env$json
# function (json, simplify = TRUE, fill_na = FALSE, buffer_size = 1024)
# {
# json_to_r(json, simplify, fill_na, buffer_size)
# }
# <bytecode: 0x7f823b9f6830>
# <environment: namespace:jsonify>
# change bigint conversion without affecting custom json conversion methods
noctua_options(bigint = "numeric")
noctua:::athena_option_env$json
# function (json, simplify = TRUE, fill_na = FALSE, buffer_size = 1024)
# {
# json_to_r(json, simplify, fill_na, buffer_size)
# }
# <bytecode: 0x7f823b9f6830>
# <environment: namespace:jsonify>
noctua:::athena_option_env$bigint
# [1] "numeric"
# change binary conversion without affect, bigint or json methods
noctua_options(binary = "character")
noctua:::athena_option_env$json
# function (json, simplify = TRUE, fill_na = FALSE, buffer_size = 1024)
# {
# json_to_r(json, simplify, fill_na, buffer_size)
# }
# <bytecode: 0x7f823b9f6830>
# <environment: namespace:jsonify>
noctua:::athena_option_env$bigint
# [1] "numeric"
noctua:::athena_option_env$binary
# [1] "character"
# no conversion for json objects
con2 <- dbConnect(noctua::athena(), json = "character")
# use custom json parser
con <- dbConnect(noctua::athena(), json = jsonify::from_json)rstudio_conn_tab within dbConnect.AWS Athena uses float data type for the DDL only, noctua was wrongly parsing float data type back to R. Instead AWS Athena uses data type real in SQL functions like select cast https://docs.aws.amazon.com/athena/latest/ug/data-types.html. noctua now correctly parses real to R’s data type double (#133)AWS returns to get all results from AWS Glue catalogue (#137)dbGetPartition. This simply tidies up the default AWS Athena partition format.
library(DBI)
library(noctua)
con <- dbConnect(athena())
dbGetPartition(con, "test_df2", .format = T)
# Info: (Data scanned: 0 Bytes)
# year month day
# 1: 2020 11 17
dbGetPartition(con, "test_df2")
# Info: (Data scanned: 0 Bytes)
# partition
# 1: year=2020/month=11/day=17bigint, this is to align with other DBI interfaces i.e. RPostgres. Now bigint can be return in the possible formats: [“integer64”, “integer”, “numeric”, “character”]library(DBI)
con <- dbConnect(noctua::athena(), bigint = "numeric")When switching between the different file parsers the bigint to be represented according to the file parser i.e. data.table: “integer64” -> vroom: “I”.
Error: write_parquet requires the arrow package, please install it first and try againdbRemoveTable would error if AWS S3 files for Athena table have been removed:Error in seq.default(1, length(l), 1000) : wrong sign in 'by' argumentNow a warning message will be returned:
Warning message:
Failed to remove AWS S3 files from: "s3://{bucket}/{prefix}/". Please check if AWS S3 files exist.dbRemoveTable now removes AWS S3 objects using delete_objects instead of delete_object. This allows noctua to delete AWS S3 files in batches. This will reduce the number of api calls to AWS and comes with a performance improvement.
library(DBI)
library(data.table)
X <- 1010
value <- data.table(x = 1:X,
y = sample(letters, X, replace = T),
z = sample(c(TRUE, FALSE), X, replace = T))
con <- dbConnect(noctua::athena())
# create a removable table with 1010 parquet files in AWS S3.
dbWriteTable(con, "rm_tbl", value, file.type = "parquet", overwrite = T, max.batch = 1)
# old method: delete_object
system.time({dbRemoveTable(con, "rm_tbl", confirm = T)})
# user system elapsed
# 31.004 8.152 115.906
# new method: delete_objects
system.time({dbRemoveTable(con, "rm_tbl", confirm = T)})
# user system elapsed
# 17.319 0.370 22.709 sql_escape_date into dplyr_integration.R backend (RAthena: # 121).use_deprecated_int96_timestamps set to TRUE. This puts POSIXct data type in to java.sql.Timestamp compatible format, such as yyyy-MM-dd HH:mm:ss[.f...]. Thanks to Christian N Wolz for highlight this issue.dbRemoveTable will ask the user twice to confirm if they wish to remove the backend files:Info: The S3 objects in prefix will be deleted:
s3://bucket/path/schema/table
Info: The S3 objects in prefix will be deleted:
s3://bucket/path/schema/tableTo overcome this dbRemoveTable will opt for paws::s3()$list_objects_v2 instead of paws::s3()$list_objects when listing s3 objects to be deleted. This allows noctua to iterate over AWS s3 prefix using tokens, instead of deleting objects in chunks. * s3_upload_location simplified how s3 location is built. Now s3.location parameter isn’t affected and instead only additional components e.g. name, schema and partition. * dbplyr v-2.0.0 function in_schema now wraps strings in quotes, this breaks db_query_fields.AthenaConnection. Now db_query_fields.AthenaConnection removes any quotation from the string so that it can search AWS GLUE for table metadata. (#117)
start_query_execution parameter ClientRequestToken. This so that the ClientRequestToken is “A unique case-sensitive string used to ensure the request to create the query is idempotent (executes only once).” (#104)R has been interrupt a new parameter has been added to dbConnect, keyboard_interrupt. Example:
# Stop AWS Athena when R has been interrupted:
con <- dbConnect(noctua::athena())
# Let AWS Athena keep running when R has been interrupted:
con <- dbConnect(noctua::athena(),
keyboard_interrupt = F)noctua would return a data.frame for utility SQL queries regardless of backend file parser. This is due to AWS Athena outputting SQL UTILITY queries as a text file that required to be read in line by line. Now noctua will return the correct data format based on file parser set in noctua_options for example: noctua_options("vroom") will return tibbles.dbClearResult when user doesn’t have permission to delete AWS S3 objects (#96)noctua_options contains 2 new parameters to control how noctua handles retries.dbFetch is able to return data from AWS Athena in chunk. This has been achieved by passing NextToken to AthenaResult s4 class. This method won’t be as fast n = -1 as each chunk will have to be process into data frame format.
library(DBI)
con <- dbConnect(noctua::athena())
res <- dbExecute(con, "select * from some_big_table limit 10000")
dbFetch(res, 5000)dbWriteTable opts to use alter table instead of standard msck repair table. This is to improve performance when appending to tables with high number of existing partitions.dbWriteTable now allows json to be appended to json ddls created with the Openx-JsonSerDe library.dbConvertTable brings dplyr::compute functionality to base package, allowing noctua to use the power of AWS Athena to convert tables and queries to more efficient file formats in AWS S3 (RAthena: # 37).dplyr::compute to give same functionality of dbConvertTable
region_name check before making a connection to AWS Athena (RAthena: # 110)dbWriteTable would throw throttling error every now and again, retry_api_call as been built to handle the parsing of data between R and AWS S3.dbWriteTable did not clear down all metadata when uploading to AWS Athena
dbWriteTable added support ddl structures for user who have created ddl’s outside of noctua
noctua retry functionality\dontrun (#91)pyathena, noctua_options now has a new parameter cache_size. This implements local caching in R environments instead of using AWS list_query_executions. This is down to dbClearResult clearing S3’s Athena output when caching isn’t disablednoctua_options now has clear_cache parameter to clear down all cached data.dbRemoveTable now utilise AWS Glue to remove tables from AWS Glue catalog. This has a performance enhancement:
library(DBI)
con = dbConnect(noctua::athena())
# upload iris dataframe for removal test
dbWriteTable(con, "iris2", iris)
# Athena method
system.time(dbRemoveTable(con, "iris2", confirm = T))
# user system elapsed
# 0.247 0.091 2.243
# upload iris dataframe for removal test
dbWriteTable(con, "iris2", iris)
# Glue method
system.time(dbRemoveTable(con, "iris2", confirm = T))
# user system elapsed
# 0.110 0.045 1.094 dbWriteTable now supports uploading json lines (http://jsonlines.org/) format up to AWS Athena (#88).
library(DBI)
con = dbConnect(noctua::athena())
dbWriteTable(con, "iris2", iris, file.type = "json")
dbGetQuery(con, "select * from iris2")dbConnect didn’t correct pass .internal metadata for paws objects.computeHostName & computeDisplayName now get region name from info object from dbConnect S4 class.dbWriteTable appending to existing table compress file type was incorrectly return.Rstudio connection tab comes into an issue when Glue Table isn’t stored correctly (RAthena: # 92)writeBin: Only 2^31 - 1 bytes can be written in a single call (and that is the maximum capacity of a raw vector on 32-bit platforms). This means that it will error out with large raw connections. To over come this writeBin can be called in chunks. If readr is available on system then readr::write_file is used for extra speed.
library(readr)
library(microbenchmark)
# creating some dummy data for testing
X <- 1e8
df <-
data.frame(
w = runif(X),
x = 1:X,
y = sample(letters, X, replace = T),
z = sample(c(TRUE, FALSE), X, replace = T))
write_csv(df, "test.csv")
# read in text file into raw format
obj <- readBin("test.csv", what = "raw", n = file.size("test.csv"))
format(object.size(obj), units = "auto")
# 3.3 Gb
# writeBin in a loop
write_bin <- function(
value,
filename,
chunk_size = 2L ^ 20L) {
total_size <- length(value)
split_vec <- seq(1, total_size, chunk_size)
con <- file(filename, "a+b")
on.exit(close(con))
sapply(split_vec, function(x){writeBin(value[x:min(total_size,(x+chunk_size-1))],con)})
invisible(TRUE)
}
microbenchmark(writeBin_loop = write_bin(obj, tempfile()),
readr = write_file(obj, tempfile()),
times = 5)
# Unit: seconds
# expr min lq mean median uq max neval
# R_loop 41.463273 41.62077 42.265778 41.908908 42.022042 44.313893 5
# readr 2.291571 2.40495 2.496871 2.542544 2.558367 2.686921 5sql_translate_env (RAthena: # 44)
# Before
translate_sql("2019-01-01", con = con)
# '2019-01-01'
# Now
translate_sql("2019-01-01", con = con)
# DATE '2019-01-01'fwrite (>=1.12.4) https://github.com/Rdatatable/data.table/blob/master/NEWS.md
paste/paste0 would use default dplyr:sql-translate-env (concat_ws). paste0 now uses Presto’s concat function and paste now uses pipes to get extra flexibility for custom separating values.
# R code:
paste("hi", "bye", sep = "-")
# SQL translation:
('hi'||'-'||'bye')append set to TRUE then existing s3.location will be utilised (RAthena: # 73)db_compute returned table name, however when a user wished to write table to another location (RAthena: # 74). An error would be raised: Error: SYNTAX_ERROR: line 2:6: Table awsdatacatalog.default.temp.iris does not exist This has now been fixed with db_compute returning dbplyr::in_schema.
library(DBI)
library(dplyr)
con <- dbConnect(noctua::athena())
tbl(con, "iris") %>%
compute(name = "temp.iris")dbListFields didn’t display partitioned columns. This has now been fixed with the call to AWS Glue being altered to include more metadata allowing for column names and partitions to be returned.dbListFields
dbStatistics is a wrapper around paws get_query_execution to return statistics for noctua::dbSendQuery resultsdbGetQuery has new parameter statistics to print out dbStatistics before returning Athena results.noctua_options
vroom has been restricted to >= 1.2.0 due to integer64 support and changes to vroom apidplyr::tbl when calling Athena when using the ident method (#64):
library(DBI)
library(dplyr)
con <- dbConnect(noctua::athena())
# ident method:
t1 <- system.time(tbl(con, "iris"))
# sub query method:
t2 <- system.time(tbl(con, sql("select * from iris")))
# ident method
# user system elapsed
# 0.082 0.012 0.288
# sub query method
# user system elapsed
# 0.993 0.138 3.660 data.table to vroom. From now on it is possible to change file parser using noctua_options for example:
library(noctua)
noctua_options("vroom")dbGetTables that returns Athena hierarchy as a data.framedbWriteTable append parameter checks and uses existing AWS Athena DDL file type. If file.type doesn’t match Athena DDL file type then user will receive a warning message:
warning('Appended `file.type` is not compatible with the existing Athena DDL file type and has been converted to "', File.Type,'".', call. = FALSE)INTEGER being incorrectly translated in sql_translate_env.R
as.character was getting wrongly translated (RAthena: # 45)data-transfer
dbRemoveTable new parameters are added in unit testsql_translate_env until test to cater bug fixdbRemoveTable can now remove S3 files for AWS Athena table being removed.tolower conversion due to request (RAthena: # 41)dbWriteTable now will split gzip compressed files to improve AWS Athena performance. By default gzip compressed files will be split into 20.Performance results
library(DBI)
X <- 1e8
df <- data.frame(w =runif(X),
x = 1:X,
y = sample(letters, X, replace = T),
z = sample(c(TRUE, FALSE), X, replace = T))
con <- dbConnect(noctua::athena())
# upload dataframe with different splits
dbWriteTable(con, "test_split1", df, compress = T, max.batch = nrow(df), overwrite = T) # no splits
dbWriteTable(con, "test_split2", df, compress = T, max.batch = 0.05 * nrow(df), overwrite = T) # 20 splits
dbWriteTable(con, "test_split3", df, compress = T, max.batch = 0.1 * nrow(df), overwrite = T) # 10 splitsAWS Athena performance results from AWS console (query executed: select count(*) from .... ):
library(DBI)
X <- 1e8
df <- data.frame(w =runif(X),
x = 1:X,
y = sample(letters, X, replace = T),
z = sample(c(TRUE, FALSE), X, replace = T))
con <- dbConnect(noctua::athena())
dbWriteTable(con, "test_split1", df, compress = T, overwrite = T) # default will now split compressed file into 20 equal size files.Added information message to inform user about what files have been added to S3 location if user is overwriting an Athena table.
dbWriteTable
POSIXct to Athena. This class was convert incorrectly and AWS Athena would return NA instead. noctua will now correctly convert POSIXct to timestamp but will also correct read in timestamp into POSIXct
NA in string format. Before noctua would return NA in string class as "" this has now been fixed.noctua would translate output into a vector with current the method dbFetch n = 0.sql_translate_env. Previously noctua would take the default dplyr::sql_translate_env, now noctua has a custom method that uses Data types from: https://docs.aws.amazon.com/athena/latest/ug/data-types.html and window functions from: https://docs.aws.amazon.com/athena/latest/ug/functions-operators-reference-section.html
upload_data has been rebuilt and removed the old “horrible” if statement with paste now the function relies on sprintf to construct the s3 location path. This method now is a lot clearer in how the s3 location is created plus it enables a dbWriteTable to be simplified. dbWriteTable can now upload data to the default s3_staging directory created in dbConnect this simplifies dbWriteTable to :
library(DBI)
con <- dbConnect(noctua::athena())
dbWriteTable(con, "iris", iris)config = list() parameter is paws objectsBigInt are now passed correctly into integer64
AthenaResult returned: Error in call[[2]] : object of type 'closure' is not subsettable. The function do.call was causing the issue, to address this do.call has been removed and the helper function request has been broken down into ResultConfiguration to return a single component of start_query_execution
do.call have been broken down due to error: Error in call[[2]] : object of type 'closure' is not subsettable
data.table is now used as the default file parser data.table::fread / data.table::fwrite. This isn’t a breaking change as data.table was used before however this change makes data.table to default file parser.