subscribeTable

subscribeTable([server],tableName,[actionName],[offset=-1],handler,[msgAsTable=false],[batchSize=0],[throttle=1],[hash=-1],[reconnect=false],[filter],[persistOffset=false],[timeTrigger=false],[handlerNeedMsgId=false],[raftGroup],[userId=""],[password=""]

Subscribe to a stream table on a local or remote server from a client node. We can also specify a function to process the subscribed data.

Return the subscription topic, which is a combination of the alias of the node where the stream table is located, stream table name, and the subscription task name (if actionName is specified) separated by "_". If the subscription topic already exists, the function throws an exception.

• If batchSize is specified, handler will be triggered if either the number of unprocessed messages reaches batchSize or the duration of time since the last time handler was triggered reaches throttle seconds.

• If the subscribed table is overwritten, to keep the subscription we need to cancel the subscription with command unsubscribeTable and then subscribe to the new table.

• With the high availability subscription enabled, a leader switch or cluster restart in the raft group of subscribers may log off a user. Since guests have no privilege to write to a DFS table, the writing will be interrupted (the current user can be identified by function getCurrentSessionAndUser). If parameters userId and password are specified, the system will attempt to log in after the user is logged out accidentally to make sure the subscribed data can be written to a DFS table. Note that since DolphinDB 2.00.10.10, users can determine whether to limit the number of failed login attempts by setting the parameter enhancedSecurityVerification. If it is not specified, no limit will be applied; if it is set to true, a user's account will be locked for 10 minutes if the user enters the wrong password 5 times in a minute.

Only tableName and handler are required. All the other parameters are optional.

server is a string indicating the alias or the remote connection handle of a server where the stream table is located. If it is unspecified or an empty string (""), it means the local instance.

tableName is a string indicating the name of the shared stream table on the aforementioned server.

actionName is a string indicating subscription task name. It starts with a letter and can have letters, digits, and underscores. It must be specified if multiple subscriptions on the same node subscribe to the same stream table.

offset is an integer indicating the position of the first message where the subscription begins. A message is a row of the stream table. Offset is relative to the first row of the stream table when it is created. If offset is unspecified or -1, the subscription starts with the next new message. If offset=-2, the system will get the persisted offset on disk and start subscription from there. If some rows were cleared from memory due to cache size limit, they are still considered in determining where the subscription starts.

Note that handler should be function appendForJoin, getLeftStream or getRightStream while subscribing to a stream table for the streaming join engine.

msgAsTable is a Boolean value indicating whether the subscribed data is ingested into handler as a table or as a tuple. If msgAsTable=true, the subscribed data is ingested into handler as a table and can be processed with SQL statements. The default value is false, which means the subscribed data is ingested into handler as a tuple of columns.

batchSize is an integer indicating the number of unprocessed messages to trigger the handler. If it is positive, the handler does not process messages until the number of unprocessed messages reaches batchSize. If it is unspecified or non-positive, the handler processes incoming messages as soon as they come in.

throttle is a floating point number in seconds, indicating the maximum waiting time before the handler processes the incoming messages if the batchSize condition has not been reached. The default value is 1. This optional parameter has no effect if batchSize is not specified. To set throttle less than 1 second, you need to modify the configuration parameter subThrottle first.

hash is a hash value (a non-negative integer) indicating which subscription executor will process the incoming messages for this subscription. If it is unspecified, the system automatically assigns an executor. When we need to keep the messages synchronized from multiple subscriptions, we can set hash of all these subscriptions to be the same, so that the same executor is used to synchronize multiple data sources.

filter indicates selected values in the filtering column. The filtering column is set with function setStreamTableFilterColumn. Only the messages with filtering column values in filter are subscribed. filter does not support Boolean types.

timeTrigger is a Boolean value. If it is set to true, handler is triggered at the intervals specified by parameter throttle even if no new messages arrive. The default value is false.

handlerNeedMsgId is a Boolean value. The default value is false.

• If it is true, the parameter handler must support 2 parameters: msgBody (the messages to be ingested into the streaming engine) and msgId (the ID of the last message that has been ingested into the streaming engine). You can pass the appendMsg function to the handler after fixing the engine parameter using partial application.

• If it is false, handler must support just 1 parameter: msgBody.

userId is a string indicating a user name.

password is a string indicating the password.

Example 1. A cluster has 2 nodes: DFS_NODE1 and DFS_NODE2. We need to specify maxPubConnections and subPort in cluster.cfg to enable the publish/subscribe functionality. For example:

maxPubConnections=32
DFS_NODE1.subPort=9010
DFS_NODE1.persistenceDir=C:/DolphinDB/Data
DFS_NODE2.subPort=9011

Execute the following script on DFS_NODE1 for the following tasks:

(1) Create a shared stream table "trades_stream" with persistence in synchronous mode. At this stage table "trades_stream" has 0 rows.

n=20000000
colNames = `time`sym`qty`price
colTypes = [TIME,SYMBOL,INT,DOUBLE]
enableTableShareAndPersistence(table=streamTable(n:0, colNames, colTypes), tableName="trades_stream", asynWrite=false, cacheSize=n)
go

(2) Create a DFS table "trades". At this stage table "trades" has 0 rows.

if(existsDatabase("dfs://STREAM_TEST")){
     dropDatabase("dfs://STREAM_TEST")
}
dbDate = database("", VALUE, temporalAdd(date(today()),0..30,'d'))
dbSym= database("", RANGE, string('A'..'Z') join "ZZZZ")
db = database("dfs://STREAM_TEST", COMPO, [dbDate, dbSym])
colNames = `date`time`sym`qty`price
colTypes = [DATE,TIME,SYMBOL,INT,DOUBLE]
trades = db.createPartitionedTable(table(1:0, colNames, colTypes), "trades", `date`sym)

(3) Create a local subscription to table "trades_stream". Use a function saveTradesToDFS to save streaming data from "trades_stream" and today's date to table "trades".

def saveTradesToDFS(mutable dfsTrades, msg): dfsTrades.append!(select today() as date,* from msg)
subscribeTable(tableName="trades_stream", actionName="trades", offset=0, handler=saveTradesToDFS{trades}, msgAsTable=true, batchSize=100000, throttle=60)

(4) Create another local subscription to table "trades_stream". Calculate volume-weighted average price (vwap) with streaming data for each minute and save the result to a shared stream table "vwap_stream" with persistence in asynchronous mode.

n=1000000
tmpTrades = table(n:0, colNames, colTypes)
lastMinute = [00:00:00.000]
colNames = `time`sym`vwap
colTypes = [MINUTE,SYMBOL,DOUBLE]
enableTableShareAndPersistence(table=streamTable(n:0, colNames, colTypes), tableName="vwap_stream")
go

def calcVwap(mutable vwap, mutable tmpTrades, mutable lastMinute, msg){
    tmpTrades.append!(msg)
    curMinute = time(msg.time.last().minute()*60000l)
    t = select wavg(price, qty) as vwap from tmpTrades where time < curMinute, time >= lastMinute[0] group by time.minute(), sym
    if(t.size() == 0) return
    vwap.append!(t)
    t = select * from tmpTrades where time >= curMinute
    tmpTrades.clear!()
    lastMinute[0] = curMinute
    if(t.size() > 0) tmpTrades.append!(t)
}
subscribeTable(tableName="trades_stream", actionName="vwap", offset=0, handler=calcVwap{vwap_stream, tmpTrades, lastMinute}, msgAsTable=true, batchSize=100000, throttle=60)

Execute the following script on DFS_NODE2 to create a remote subscription to table "trades_stream". This subscription saves streaming data to a shared stream table "trades_stream_slave" with persistence in asynchronous mode.

n=20000000
colNames = `time`sym`qty`price
colTypes = [TIME,SYMBOL,INT,DOUBLE]
enableTableShareAndPersistence(table=streamTable(n:0, colNames, colTypes), tableName="trades_stream_slave", cacheSize=n)
go
subscribeTable(server="DFS_NODE1", tableName="trades_stream", actionName="slave", offset=0, handler=trades_stream_slave)

Execute the following script on DFS_NODE1 to simulate the streaming data for 3 stocks and 10 minutes. Generate 2,000,000 records for each stock in each minute. Data in one minute are inserted into the stream table "trades_stream" in 600 blocks. There is a 100 millisecond interval between 2 blocks.

n=10
ticks = 2000000
rows = ticks*3
startMinute = 09:30:00.000
blocks=600
for(x in 0:n){
    time = startMinute + x*60000 + rand(60000, rows)
    indices = isort(time)
    time = time[indices]
    sym = array(SYMBOL,0,rows).append!(take(`IBM,ticks)).append!(take(`MSFT,ticks)).append!(take(`GOOG,ticks))[indices]
    price = array(DOUBLE,0,rows).append!(norm(153,1,ticks)).append!(norm(91,1,ticks)).append!(norm(1106,20,ticks))[indices]
    indices = NULL
    blockSize = rows / blocks
    for(y in 0:blocks){
        range =pair(y * blockSize, (y+1)* blockSize)
        insert into trades_stream values(subarray(time,range), subarray(sym,range), 10+ rand(100, blockSize), subarray(price,range))
        sleep(100)
    }

    blockSize = rows % blocks
    if(blockSize > 0){
        range =pair(rows - blockSize, rows)
        insert into trades_stream values(subarray(time,range), subarray(sym,range), 10+ rand(100, blockSize), subarray(price,range))
    }
}

To check the results, run the following script on DFS_NODE1:

trades=loadTable("dfs://STREAM_TEST", `trades)
select count(*) from trades

We expect to see a result of 60,000,000

select * from vwap_stream

We expect the table "vwap_stream" has 27 rows.

Run the following script on DFS_NODE2:

select count(*) from trades_stream_slave

We expect to see a result of less than 60,000,000 as part of the table has been persisted to disk.