[英]How to improve the speed of this code?
我正在嘗試將所有googlebooks-1gram文件導入postgresql數據庫。 我為此編寫了以下Java代碼:
public class ToPostgres {
public static void main(String[] args) throws Exception {
String filePath = "./";
List<String> files = new ArrayList<String>();
for (int i =0; i < 10; i++) {
files.add(filePath+"googlebooks-eng-all-1gram-20090715-"+i+".csv");
}
Connection c = null;
try {
c = DriverManager.getConnection("jdbc:postgresql://localhost/googlebooks",
"postgres", "xxxxxx");
} catch (SQLException e) {
e.printStackTrace();
}
if (c != null) {
try {
PreparedStatement wordInsert = c.prepareStatement(
"INSERT INTO words (word) VALUES (?)", Statement.RETURN_GENERATED_KEYS
);
PreparedStatement countInsert = c.prepareStatement(
"INSERT INTO wordcounts (word_id, \"year\", total_count, total_pages, total_books) " +
"VALUES (?,?,?,?,?)"
);
String lastWord = "";
Long lastId = -1L;
for (String filename: files) {
BufferedReader input = new BufferedReader(new FileReader(new File(filename)));
String line = "";
while ((line = input.readLine()) != null) {
String[] data = line.split("\t");
Long id = -1L;
if (lastWord.equals(data[0])) {
id = lastId;
} else {
wordInsert.setString(1, data[0]);
wordInsert.executeUpdate();
ResultSet resultSet = wordInsert.getGeneratedKeys();
if (resultSet != null && resultSet.next())
{
id = resultSet.getLong(1);
}
}
countInsert.setLong(1, id);
countInsert.setInt(2, Integer.parseInt(data[1]));
countInsert.setInt(3, Integer.parseInt(data[2]));
countInsert.setInt(4, Integer.parseInt(data[3]));
countInsert.setInt(5, Integer.parseInt(data[4]));
countInsert.executeUpdate();
lastWord = data[0];
lastId = id;
}
}
} catch (SQLException e) {
e.printStackTrace();
}
}
}
}
但是,當運行它約3個小時時,它只在wordcounts表中放置了1.000.000個條目。 當我檢查整個1gram數據集中的行數時,它是500.000.000行。 所以進口一切大概需要62.5天,我可以接受它大約一周進口,但是2個月? 我認為我在這里做了一些嚴重錯誤的事情(我確實有一台24/7全天候運行的服務器,所以我實際上可以運行它這么長時間,但速度會更快XD)
編輯:這段代碼是我解決它的方式:
public class ToPostgres {
public static void main(String[] args) throws Exception {
String filePath = "./";
List<String> files = new ArrayList<String>();
for (int i =0; i < 10; i++) {
files.add(filePath+"googlebooks-eng-all-1gram-20090715-"+i+".csv");
}
Connection c = null;
try {
c = DriverManager.getConnection("jdbc:postgresql://localhost/googlebooks",
"postgres", "xxxxxx");
} catch (SQLException e) {
e.printStackTrace();
}
if (c != null) {
c.setAutoCommit(false);
try {
PreparedStatement wordInsert = c.prepareStatement(
"INSERT INTO words (id, word) VALUES (?,?)"
);
PreparedStatement countInsert = c.prepareStatement(
"INSERT INTO wordcounts (word_id, \"year\", total_count, total_pages, total_books) " +
"VALUES (?,?,?,?,?)"
);
String lastWord = "";
Long id = 0L;
for (String filename: files) {
BufferedReader input = new BufferedReader(new FileReader(new File(filename)));
String line = "";
int i = 0;
while ((line = input.readLine()) != null) {
String[] data = line.split("\t");
if (!lastWord.equals(data[0])) {
id++;
wordInsert.setLong(1, id);
wordInsert.setString(2, data[0]);
wordInsert.executeUpdate();
}
countInsert.setLong(1, id);
countInsert.setInt(2, Integer.parseInt(data[1]));
countInsert.setInt(3, Integer.parseInt(data[2]));
countInsert.setInt(4, Integer.parseInt(data[3]));
countInsert.setInt(5, Integer.parseInt(data[4]));
countInsert.executeUpdate();
lastWord = data[0];
if (i % 10000 == 0) {
c.commit();
}
if (i % 100000 == 0) {
System.out.println(i+" mark file "+filename);
}
i++;
}
c.commit();
}
} catch (SQLException e) {
e.printStackTrace();
}
}
}
}
我現在在大約15分鍾內達到了150萬行。 這對我來說足夠快了,謝謝大家!
JDBC連接默認啟用自動提交,它帶有每語句開銷。 嘗試禁用它:
c.setAutoCommit(false)
然后批量提交,類似於:
long ops = 0;
for(String filename : files) {
// ...
while ((line = input.readLine()) != null) {
// insert some stuff...
ops ++;
if(ops % 1000 == 0) {
c.commit();
}
}
}
c.commit();
如果您的表具有索引,則刪除它們,插入數據以及稍后重新創建索引可能會更快。
設置autocommit off,並且每隔10 000條記錄進行一次手動提交(查看文檔中的合理值 - 有一些限制)也可以加快速度。
自己生成索引/外鍵並跟蹤它應該比wordInsert.getGeneratedKeys();
更快wordInsert.getGeneratedKeys();
但我不確定,你的內容是否有可能。
有一種稱為“批量插入”的方法。 我不記得細節,但它是搜索的起點。
寫它來做線程,同時運行4個線程,或者將它分成幾部分(從配置文件中讀取)並將它分發給X機器並讓它們獲取數據togeather。
使用批處理語句同時執行多個插入,而不是一次執行一個INSERT。
另外,我會刪除算法的一部分,它會在每次插入words
表后更新單詞計數,而只需在插入words
完成后計算所有單詞計數。
另一種方法是進行批量插入而不是單個插入。 看到這個問題什么是最快的方式進行批量插入Postgres? 欲獲得更多信息。
創建線程
String lastWord = "";
Long lastId = -1L;
PreparedStatement wordInsert;
PreparedStatement countInsert ;
public class ToPostgres {
public void main(String[] args) throws Exception {
String filePath = "./";
List<String> files = new ArrayList<String>();
for (int i =0; i < 10; i++) {
files.add(filePath+"googlebooks-eng-all-1gram-20090715-"+i+".csv");
}
Connection c = null;
try {
c = DriverManager.getConnection("jdbc:postgresql://localhost/googlebooks",
"postgres", "xxxxxx");
} catch (SQLException e) {
e.printStackTrace();
}
if (c != null) {
try {
wordInsert = c.prepareStatement(
"INSERT INTO words (word) VALUES (?)", Statement.RETURN_GENERATED_KEYS
);
countInsert = c.prepareStatement(
"INSERT INTO wordcounts (word_id, \"year\", total_count, total_pages, total_books) " +
"VALUES (?,?,?,?,?)"
);
for (String filename: files) {
new MyThread(filename). start();
}
} catch (SQLException e) {
e.printStackTrace();
}
}
}
}
class MyThread extends Thread{
String file;
public MyThread(String file) {
this.file = file;
}
@Override
public void run() {
try {
super.run();
BufferedReader input = new BufferedReader(new FileReader(new File(file)));
String line = "";
while ((line = input.readLine()) != null) {
String[] data = line.split("\t");
Long id = -1L;
if (lastWord.equals(data[0])) {
id = lastId;
} else {
wordInsert.setString(1, data[0]);
wordInsert.executeUpdate();
ResultSet resultSet = wordInsert.getGeneratedKeys();
if (resultSet != null && resultSet.next())
{
id = resultSet.getLong(1);
}
}
countInsert.setLong(1, id);
countInsert.setInt(2, Integer.parseInt(data[1]));
countInsert.setInt(3, Integer.parseInt(data[2]));
countInsert.setInt(4, Integer.parseInt(data[3]));
countInsert.setInt(5, Integer.parseInt(data[4]));
countInsert.executeUpdate();
lastWord = data[0];
lastId = id;
}
} catch (NumberFormatException e) {
e.printStackTrace();
} catch (FileNotFoundException e) {
e.printStackTrace();
} catch (IOException e) {
e.printStackTrace();
} catch (SQLException e) {
e.printStackTrace();
}
}
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