Synapse Tables enable storage of tabular data in Synapse in a form that can be queried using a SQL-like query language.
A table has a Schema and holds a set of rows conforming to that schema.
A Schema defines a series of Column of the following types: STRING, DOUBLE, INTEGER, BOOLEAN, DATE, ENTITYID, FILEHANDLEID, LINK, LARGETEXT, USERID
Preliminaries:
library(synapser) synLogin() # Create a new project # use hex_digits to generate random string hex_digits <- c(as.character(0:9), letters[1:6]) projectName <- sprintf("My unique project %s", paste0(sample(hex_digits, 32, replace = TRUE), collapse = "")) project <- Project(projectName) project <- synStore(project)
Let’s say we have some data stored in an R data.frame:
genes <- data.frame( Name = c("foo", "arg", "zap", "bah", "bnk", "xyz"), Chromosome = c(1, 2, 2, 1, 1, 1), Start = c(12345, 20001, 30033, 40444, 51234, 61234), End = c(126000, 20200, 30999, 41444, 54567, 68686), Strand = c("+", "+", "-", "-", "+", "+"), TranscriptionFactor = c(F, F, F, F, T, F), Time = as.POSIXlt(c("2017-02-14 11:23:11.024", "1970-01-01 00:00:00.000", "2018-10-01 00:00:00.000", "2020-11-03 04:59:59.999", "2011-12-16 06:23:11.139", "1999-03-18 21:03:33.044"), tz = "UTC", format = "%Y-%m-%d %H:%M:%OS"))
To create a Table with name "My Favorite Genes" with project
as parent:
table <- synBuildTable("My Favorite Genes", project, genes) table$schema
synBuildTable
creates a Table Schema based on the data and returns a Table object that can be stored in Synapse using synStore()
. To create a custom Table Schema, defines the columns of the table:
cols <- list( Column(name = "Name", columnType = "STRING", maximumSize = 20), Column(name = "Chromosome", columnType = "STRING", maximumSize = 20), Column(name = "Start", columnType = "INTEGER"), Column(name = "End", columnType = "INTEGER"), Column(name = "Strand", columnType = "STRING", enumValues = list("+", "-"), maximumSize = 1), Column(name = "TranscriptionFactor", columnType = "BOOLEAN"), Column(name = "Time", columnType = "DATE")) schema <- Schema(name = "My Favorite Genes", columns = cols, parent = project) table <- Table(schema, genes)
Let’s store that in Synapse:
table <- synStore(table) tableId <- table$tableId
The Table() function takes two arguments, a schema object, or a Table ID and data in some form, which can be:
We now have a table populated with data. Let’s try to query:
results <- synTableQuery(sprintf('select * from %s where Chromosome=1 and Start < 41000 and "End" > 20000', tableId), resultsAs = 'csv')
synTableQuery()
downloads the data and saves it to a csv file at location:
results$filepath
To load the data into an R data.frame:
df <- as.data.frame(results)
Once the schema is settled, changes come in two flavors: appending new rows and updating existing ones.
Changing data in a table requires row IDs and version numbers for each row to be modified (called ROW_ID
and ROW_VERSION
). We get those by querying before updating. Minimizing change sets to contain only rows that actually change will make processing faster. Appending new rows can be accomplished by leaving values for ROW_ID
and ROW_VERSION
blank.
Appending new rows is fairly straightforward. To continue the previous example, we might add some new genes:
moreGenes <- data.frame( Name = c("abc", "def"), Chromosome = c(2, 2), Start = c(12345, 20001), End = c(126000, 20200), Strand = c("+", "+"), TranscriptionFactor = c(F, F), Time = as.POSIXlt(c("2070-01-12 03:53:12.169", "2018-05-03 12:03:33.464"), tz = "UTC", format = "%Y-%m-%d %H:%M:%OS")) synStore(Table(tableId, moreGenes))
For example, let’s update the names of some of our favorite genes:
results <- synTableQuery(sprintf("select * from %s where Chromosome='1'", tableId)) df <- as.data.frame(results) df["Name"] <- c("rzing", "zing1", "zing2", "zing3")
Let’s save that:
table <- Table(tableId, df) table <- synStore(table)
Now, query the table again to see your changes:
results <- synTableQuery(sprintf("select * from %s limit 10", tableId)) as.data.frame(results)
One other piece of information required for making changes to tables is the etag, which is used by the Synapse server to prevent concurrent users from making conflicting changes through a technique called optimistic concurrency. This comes as a result of running synTableQuery
. In the example above, you could see the etag by running results$etag
- but you should never have to use it directly. In case of a conflict, your update may be rejected. You then have to do another query and try your update again.
Adding columns can be done using the methods Schema$addColumn() or addColumns() on the Schema object:
schema <- synGet(tableId) newColumn <- synStore(Column(name = "Note", columnType = "STRING", maximumSize = 20)) schema$addColumn(newColumn) schema <- synStore(schema)
In the example above, we do know what newColumn
is. While working with other columns, you can retrieve all columns a table has by using synGetTableColumns()
:
columns <- as.list(synGetTableColumns(tableId))
You can then explore the list of columns to find the column you want to modify.
Renaming or otherwise modifying a column involves removing the column and adding a new column:
notesColumn <- synStore(Column(name = "Notes", columnType = "STRING", maximumSize = 20)) schema <- synGet(tableId) schema$removeColumn(newColumn) schema$addColumn(notesColumn) schema <- synStore(schema)
Now we can set the values for the new column:
results <- synTableQuery(sprintf("SELECT * FROM %s", tableId)) data <- as.data.frame(results) data["Notes"] <- c("check name", NA, NA, NA, "update test", NA, NA, NA) synStore(Table(tableId, data))
Column "Notes" has type STRING with "maximumSize" set to 20. We cannot add a new row with "Notes" as "a very looooooooong note" since it has more than 20 characters. Let"s change the ColumnType to "STRING" with "maximumSize" set to 100:
# getting the existing table metadata and data originalSchema <- synGet(tableId) oldQueryResults <- synTableQuery(sprintf("SELECT * FROM %s", tableId)) oldData <- as.data.frame(oldQueryResults) # remove the column originalSchema$removeColumn(notesColumn) newSchema <- synStore(originalSchema) # create a new Column newCol <- Column(name = "Notes", columnType = "STRING", maximumSize = 100) # add the new column to the new table newSchema$addColumn(newCol) newSchema <- synStore(newSchema) # copy the data over to the new column newQueryResults <- synTableQuery(sprintf("SELECT * FROM %s", newSchema$properties$id)) newData <- as.data.frame(newQueryResults) newData["Notes"] <- oldData["Notes"] # save the change synStore(Table(tableId, newData)) # add the new data moreGenes <- data.frame( Name = c("not_sure"), Chromosome = c(2), Start = c(12345), End = c(126000), Strand = c("+"), TranscriptionFactor = c(F), Time = as.POSIXlt("2014-07-03 20:12:44.000", tz = "UTC", format = "%Y-%m-%d %H:%M:%OS"), Notes = c("a very looooooooong note")) synStore(Table(tableId, moreGenes))
To access a column that you do not have a reference to, please see:
?synGetColumn ?synGetColumns
In Synapse tables, the DATE type is stored as a timestamp integer, equivalent to the number of milliseconds that have passed since 1970-01-01 00:00:00 UTC. R has built in POSIXt
types that are similar, though they are numerics that store time in seconds. When values of type POSIXt
are uploaded to Synapse tables in synapser, values are automatically converted to millisecond timestamps. Conversely, values in Synapse table columns of type DATE are automatically converted to POSIXlt
by synapser.
When adding or changing dates, they may be POSIXt
times.
timestamp <- as.numeric(as.POSIXlt("1980-01-01", tz = "UTC", format = "%Y-%m-%d")) * 1000 results <- synTableQuery(sprintf("select * from %s where \"Time\" < %.0f", tableId, timestamp)) df <- as.data.frame(results) df$Time <- as.POSIXlt("2015-07-04 05:22", tz = "UTC", format = "%Y-%m-%d %H:%M") synStore(Table(tableId, moreGenes))
Dates may also be submitted in timestamp milliseconds:
results <- synTableQuery(sprintf("select * from %s where Name='zap'", tableId)) df <- as.data.frame(results) df$Time <- timestamp synStore(Table(tableId, moreGenes))
Note that using POSIXlt
is strongly preferred over POSIXct
, because POSIXct
does not store values with enough precision to reliably recover milliseconds. For more information, see the R documentation:
?as.POSIXlt
Synapse tables support a special column type called ‘File’ which contain a file handle, an identifier of a file stored in Synapse. Here’s an example of how to upload files into Synapse, associate them with a table and read them back later:
newCols <- list( Column(name = "artist", columnType = "STRING", maximumSize = 50), Column(name = "album", columnType = "STRING", maximumSize = 50), Column(name = "year", columnType = "INTEGER"), Column(name = "catalog", columnType = "STRING", maximumSize = 50), Column(name = "covers", columnType = "FILEHANDLEID")) newSchema <- synStore(Schema(name = "Jazz Albums", columns = newCols, parent = project)) newData <- data.frame( artist = c("John Coltrane", "Sonny Rollins", "Sonny Rollins", "Kenny Burrel"), album = c("Blue Train", "Vol. 2", "Newk's Time", "Kenny Burrel"), year = c(1957, 1957, 1958, 1956), catalog = c("BLP 1577", "BLP 1558", "BLP 4001", "BLP 1543") ) # writing some temp files to upload or pointing to existing files in your system files <- c("coltraneBlueTrain.jpg", "rollinsBN1558.jpg", "rollinsBN4001.jpg", "burrellWarholBN1543.jpg") # upload to filehandle service files <- lapply(files, function (f) { cat(f, file = f) synUploadFileHandle(f, project) }) # get the filehandle ids fileHandleIds <- lapply(files, function(f) f$id) newData["covers"] <- fileHandleIds newTable <- synStore(Table(newSchema$properties$id, newData))
To download attached files in a table:
result <- synTableQuery(sprintf("select * from %s", newTable$tableId)) data <- synDownloadTableColumns(result, columns = list("covers"))
A table schema is a Synapse entity. Annotations on table works the same way as annotations on any other entity types.
To set annotation on table, use synStore() on the schema. Note, forceVersion=False
will not re version the file
and setting the annotations this way will wipe the old versions.
schema <- synGet(tableId) schema$annotations = list(temp = "test") synStore(schema, forceVersion = FALSE)
To view annotations on table, retrieve the schema:
schema <- synGet(tableId) schema$annotations
Please visit synapser vignettes to read more about annotations.
Query for the rows you want to delete and call synDelete on the results:
results <- synTableQuery(sprintf("select * from %s where Chromosome='2'", tableId)) deleted <- synDelete(results)
Deleting the schema deletes the whole table and all rows:
synDelete(schema)
The query language is quite similar to SQL select statements, except that joins are not supported. The documentation for the Synapse API has lots of query examples.
For more details see the native reference documentation, e.g.:
?Schema ?Column ?Row ?Table
synDelete(project) fileHandleIds <- lapply(files, function(f) file.remove(f$fileName))
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