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I have scenario when I have to iterate through multiple tables in quite big sqlite database. In tables I store informations about planet position on sky through years. So e.g. for Mars I have tables Mars_2000, Mars_2001 and so on. Table structure is always the same:
|id:INTEGER|date:TEXT|longitude:REAL|
Thing is that for certain task I need to iterate through this tables, which cost much time (for more than 10 queries it's painful).
I suppose that if I merge all tables with years to one big table performance might be better as one query through one big table is better than 50 through smaller tables. I wanted to make sure that this might work, as database is humongous (around 20Gb), and reshaping it would cost a while.
Is this plan I just described viable? Is there any other solution for such case?
It might be helpfull so I attach function that produces my SQL query that is unique for each table:
pub fn transition_query(
select_param: &str, // usually asterix
table_name: &str, // table I'd like to query
birth_degree: &f64, // constant number
wanted_degree: &f64, // another constant number
orb: &f64, // another constant number
upper_date_limit: DateTime<Utc>, // casts to SQL-like string
lower_date_limit: DateTime<Utc>, // casts to SQL-like string
) -> String {
let parsed_upper_date_limit = CelestialBodyPosition::parse_date(upper_date_limit);
let parsed_lower_date_limit = CelestialBodyPosition::parse_date(lower_date_limit);
return format!("
SELECT *,(SECOND_LAG>60 OR SECOND_LAG IS NULL) AS TRANSIT_START, (SECOND_LEAD > 60 OR SECOND_LEAD IS NULL) AS TRANSIT_END, time FROM (
SELECT
*,
UNIX_TIME - LAG(UNIX_TIME,1) OVER (ORDER BY time) as SECOND_LAG,
LEAD(UNIX_TIME,1) OVER (ORDER BY time) - UNIX_TIME as SECOND_LEAD FROM (
SELECT {select_param},
DATE(time) as day_scoped_date,
CAST(strftime('%s', time) AS INT) AS UNIX_TIME,
longitude
FROM {table_name}
WHERE ((-{orb} <= abs(realModulo(longitude -{birth_degree} -{wanted_degree},360))
AND abs(realModulo(longitude -{birth_degree} -{wanted_degree},360)) <= {orb})
OR
(-{orb} <= abs(realModulo(longitude -{birth_degree} +{wanted_degree},360))
AND abs(realModulo(longitude -{birth_degree} +{wanted_degree},360)) <= {orb}))
AND time < '{parsed_upper_date_limit}' AND time > '{parsed_lower_date_limit}'
)
) WHERE (TRANSIT_START AND NOT TRANSIT_END) OR (TRANSIT_END AND NOT TRANSIT_START) ;
");
}
I solved the issue programmatically. Whole thing was done with Rust and r2d2_sqlite library. I'm still doing a lot of queries, but now it's done in threads. It allowed me to reduce execution time from 25s to around 3s. Here's the code:
use std::sync::mpsc;
use std::thread;
use r2d2_sqlite::SqliteConnectionManager;
use r2d2;
let manager = SqliteConnectionManager::file("db_path");
let pool = r2d2::Pool::builder().build(manager).unwrap();
let mut result: Vec<CelestialBodyPosition> = vec![]; // Vector of structs
let (tx, rx) = mpsc::channel(); // Allows ansynchronous communication
let mut children = vec![]; //vector of join handlers (not sure if needed at all
for query in queries {
let pool = pool.clone(); // For each loop I clone connection to databse
let inner_tx = tx.clone(); // and messager, as each thread should have spearated one.
children.push(thread::spawn(move || {
let conn = pool.get().unwrap();
add_real_modulo_function(&conn); // this adds custom sqlite function I needed
let mut sql = conn.prepare(&query).unwrap();
// this does query, and maps result to my internal type
let positions: Vec<CelestialBodyPosition> = sql
.query_map(params![], |row| {
Ok(CelestialBodyPosition::new(row.get(1)?, row.get(2)?))
})
.unwrap()
.map(|position| position.unwrap())
.collect();
// this sends partial result to receiver
return inner_tx.send(positions).unwrap();
}));
}
// first messenger has to be dropped, otherwise program will wait for its input
drop(tx);
for received in rx {
result.extend(received); // combine all results
}
return result;
As you can see no optimization happened from sqlite site, which kinda makes me feel I'm doing something wrong, but for now it's alright. It might be good to press some more control over amount of spawned threads.
the code as:
Dataset<Row> invoke = spark.read().format("jdbc").option("url", url)
.option("dbtable", query)
.option("partitionColumn", "id")
.option("numPartitions", "5")
.option("lowerBound", Long.MIN_VALUE)
.option("upperBound", Long.MAX_VALUE)
.load();
when the gp table is a large table,oom will occur
how can handle the large table?
I have an endpoint lets say /order/ where i can send json object(my order), which contains some products etc, so my problem is i have to first save the order and wait for the order id back from the db and then save my products with this new order id( we are talking many to many relation thats why theres another table)
Consider this controller method
def postOrder = Action(parse.json[OrderRest]) { req => {
Created(Json.toJson(manageOrderService.insertOrder(req.body)))
}
}
this is how my repo methods look like
def addOrder(order: Order) = db.run {
(orders returning orders) += order
}
how can i chain db.runs to first insert order, get order id and then insert my products with this order id i just got?
im thinking about putting some service between my controller and repo, and managing those actions there, but i have no idea where to start
You can use for to chain database operations. Here is an example of adding a table to a db by adding a header row to represent the table and then adding the data rows. In this case it is a simple table containing (age, value).
/** Add a new table to the database */
def addTable(name: String, table: Seq[(Int, Int)]) = {
val action = for {
key <- (Headers returning Headers.map(_.tableId)) += HeadersRow(0, name)
_ <- Values ++= table.map { case (age, value) => ValuesRow(key, age, value) }
} yield key
db.run(action.transactionally)
}
This is cut down from the working code, but it should give the idea of how to do what you want. The first for statement would generate the order id and then the second statement would add the order with that order id.
This is done transactionally so that the new order will not be created unless the order data is valid (in database terms).
I am using DataFu to baggroup my a bag. The is as following:
pvlist_grp = GROUP pvlist by uid;
uid_vid_pv = FOREACH pvlist_grp {
vids = FOREACH pvlist GENERATE date, vid;
GENERATE uid,
vids as vid,
BagGroup(pvlist.(date, uid, vid), pvlist.date) as grouped;
}
uid_vid_pv: {uid: chararray,vid: {(date: chararray,vid: chararray)},grouped: {(group: chararray,{(date: chararray,uid: chararray,vid: chararray)})}}
When I dump the first 10, I see all the vids that contains (date, vid) for each uid. However, the grouped shows other uid records. For example:
(60,{(20160103,255),(20160103,255),(20160103,257),(20160103,255),(20160101,252)},{(20160103,{(20160103,21,18),(20160103,21,453),(20160103,21,452),(20160103,21,67),(20160103,21,18),(20160103,21,455),(20160103,21,43),(20160103,21,453),(20160103,21,16),(20160103,21,45),(20160103,21,18),(20160103,21,18),(20160103,21,67),(20160103,21,455),.............})})
The dumped result shows the baggroup with other uid data in it. It groups the entire vid bags from all uids, but I want it just per uid.
The idea result should be like:
(60,{(20160103,255),(20160103,255),(20160103,257),(20160103,255),(20160101,252)},{(20160103,{(20160103,255),(20160103,255),(20160103,257),(20160103,255)}),(20160101,{(20160101,252)})})
Any Help why? I am using pig 1.2.0.
UPdate:
Looks like the BagGroup sort of calls operation from memory. So the first uid BagGroup is always correct, then it adds the bags to operate together from what has been processed before. IE. if the first record is uid 21, then the BagGroup has all the 21 results Grouped. Next, if the second record uid 60, then the BagGroup will output 21 and 60 results together.
I had exactly the same problem. In order to solve it, I had to modify the BagGroup UDF (ver.1.2.0). Adding groups.clear(); at the beginning of the exec method resolves this issue.
#SuppressWarnings("unchecked")
#Override
public DataBag exec(Tuple input) throws IOException {
fieldNames = (List<String>)getInstanceProperties().get(FIELD_NAMES_PROPERTY);
DataBag inputBag = (DataBag)input.get(0);
groups.clear();
for (Tuple tuple : inputBag) {
Tuple key = extractKey(tuple);
addGroup(key, tuple);
}
}
i'm searching for a long time now to solve my problem but nearly found nothing helpful.
Hopefully some of you can give me a tip.
I have a relation A with the following format: username, timestamp, ip
For example:
Harald 2014-02-18T16:14:49.503Z 123.123.123.123
Harald 2014-02-18T16:14:51.503Z 123.123.123.123
Harald 2014-02-18T16:14:55.503Z 321.321.321.321
And i want to find out, who changed his ip adress in less then 5 seconds. So the second and the third row should be interesting.
I want do group the relation by username und want to compare the timestamp of the actuall row with the next row. if the ip adress isnt the same and the timestamp is less then 5 seconds bigger, this should be at the output.
could someone help me with that issue?
regards.
first i want to thank you for your time.
but i actually stuck at the Sessionize part.
this is my data comming in:
aoebcu 2014-02-19T14:23:17.503Z 220.61.65.25
aoebcu 2014-02-19T14:23:14.503Z 222.117.144.19
aoebcu 2014-02-19T14:23:14.503Z 222.117.144.19
jekgru 2014-02-19T14:23:14.503Z 213.56.157.109
zmembx 2014-02-19T14:23:12.503Z 199.188.198.91
qhixcg 2014-02-19T14:23:11.503Z 203.40.104.119
and my code till now looks like this:
hijack_Reduced = FOREACH finalLogs GENERATE ClientUserName, timestamp, OriginalClientIP;
hijack_Filtered = FILTER hijack_Reduced BY OriginalClientIP != '-';
hijack_Sessionized = FOREACH (GROUP hijack_Filtered BY ClientUserName) {
views = ORDER hijack_Filtered BY timestamp;
GENERATE FLATTEN(Sessionize(views)) AS (ClientUserName,timestamp,OriginalClientIP,session_id);
}
but when i run this script, i got the following error Message:
15:36:22 ERROR -
org.apache.pig.tools.pigstats.SimplePigStats.setBackendException(542)
| ERROR 0: Exception while executing [POUserFunc (Name:
POUserFunc(datafu.pig.sessions.Sessionize)[bag] - scope-199 Operator
Key: scope-199) children: null at []]:
java.lang.IllegalArgumentException: Invalid format: "aoebcu"
i already tried a lot, but nothing worked.
do you got an idea?
Regards
While you could write a UDF for this, you can actually make use of the UDFs already available in Apache DataFu to solve this.
My solution involves applying sessionization to the data. Basically you look at consecutive events and assign each event a session ID. If the time elapsed between two events exceeds a specified amount of time, in your case 5 seconds, then the next event gets a new session ID. Otherwise consecutive events get the same session ID. Once each event is assigned its session ID the rest is easy. We group by session ID and look for sessions that have more than one distinct IP address.
I'll walk through my solution.
Suppose you have the following input data. Both Harold and Kumar change their IP addresses. But Harold does it within 5 seconds, while Kumar does not. So the output of our script should just be simply "Harold".
Harold,2014-02-18T16:14:49.503Z,123.123.123.123
Harold,2014-02-18T16:14:51.503Z,123.123.123.123
Harold,2014-02-18T16:14:55.503Z,321.321.321.321
Kumar,2014-02-18T16:14:49.503Z,123.123.123.123
Kumar,2014-02-18T16:14:55.503Z,123.123.123.123
Kumar,2014-02-18T16:15:05.503Z,321.321.321.321
Load the data
data = LOAD 'input' using PigStorage(',')
AS (user:chararray,time:chararray,ip:chararray);
Now define a couple UDFs from DataFu. The Sessionize UDF performs sessionization as I described earlier. The DistinctBy UDF will be used to find the distinct IP addresses within each session.
define Sessionize datafu.pig.sessions.Sessionize('5s');
define DistinctBy datafu.pig.bags.DistinctBy('1');
Group the data by user, sort by time, and apply the Sessonize UDF. Note that the timestamp must be the first field, as this is what Sessionize expects. This UDF appends a session ID to each tuple.
data = FOREACH data GENERATE time,user,ip;
data_sessionized = FOREACH (GROUP data BY user) {
views = ORDER data BY time;
GENERATE flatten(Sessionize(views)) as (time,user,ip,session_id);
}
Now that the data is sessionized, we can group by the user and session. I group by user too because I want to spit this value back out. We pass the bag of events into the DistinctBy UDF. Check the documentation of this UDF for a more detailed description. But essentially we will get as many tuples as there are distinct IP addresses per session. Note that I have removed the time from the relation below. This is because 1) it isn't needed, and 2) the DistinctBy in 1.2.0 of DataFu has a bug when handling fields containing dashes, as the time field does.
data_sessionized = FOREACH data_sessionized GENERATE user,ip,session_id;
data_sessionized = FOREACH (GROUP data_sessionized BY (user, session_id)) GENERATE
group.user as user,
SIZE(DistinctBy(data_sessionized)) as distinctIpCount;
Now select all the sessions that had more than one distinct IP address and return the distinct users for these sessions.
data_sessionized = FILTER data_sessionized BY distinctIpCount > 1;
data_sessionized = FOREACH data_sessionized GENERATE user;
data_sessionized = DISTINCT data_sessionized;
This produces simply:
Harold
Here is the full source code, which you should be able to paste directly into the DataFu unit tests and run:
/**
define Sessionize datafu.pig.sessions.Sessionize('5s');
define DistinctBy datafu.pig.bags.DistinctBy('1'); -- distinct by ip
data = LOAD 'input' using PigStorage(',') AS (user:chararray,time:chararray,ip:chararray);
data = FOREACH data GENERATE time,user,ip;
data_sessionized = FOREACH (GROUP data BY user) {
views = ORDER data BY time;
GENERATE flatten(Sessionize(views)) as (time,user,ip,session_id);
}
data_sessionized = FOREACH data_sessionized GENERATE user,ip,session_id;
data_sessionized = FOREACH (GROUP data_sessionized BY (user, session_id)) GENERATE
group.user as user,
SIZE(DistinctBy(data_sessionized)) as distinctIpCount;
data_sessionized = FILTER data_sessionized BY distinctIpCount > 1;
data_sessionized = FOREACH data_sessionized GENERATE user;
data_sessionized = DISTINCT data_sessionized;
STORE data_sessionized INTO 'output';
*/
#Multiline private String sessionizeUserIpTest;
private String[] sessionizeUserIpTestData = new String[] {
"Harold,2014-02-18T16:14:49.503Z,123.123.123.123",
"Harold,2014-02-18T16:14:51.503Z,123.123.123.123",
"Harold,2014-02-18T16:14:55.503Z,321.321.321.321",
"Kumar,2014-02-18T16:14:49.503Z,123.123.123.123",
"Kumar,2014-02-18T16:14:55.503Z,123.123.123.123",
"Kumar,2014-02-18T16:15:05.503Z,321.321.321.321"
};
#Test
public void sessionizeUserIpTest() throws Exception
{
PigTest test = createPigTestFromString(sessionizeUserIpTest);
this.writeLinesToFile("input",
sessionizeUserIpTestData);
List<Tuple> result = this.getLinesForAlias(test, "data_sessionized");
assertEquals(result.size(),1);
assertEquals(result.get(0).get(0),"Harold");
}