hive -e 出现cannot recognize input nearXXX

本文介绍了一种在使用Hive SQL时遇到的语法错误及其解决办法。具体来说,当尝试执行包含concat_ws和collect_set函数的SQL语句时遇到了ParseException。通过调整逗号的格式和将SQL语句保存到文件中再执行两种方式解决了该问题。

 

 

做接口平台,将 hive sql放在shell里,用

 

hive -e "'

    select xxx, concat_ws(",",collect_set(字段))  字段  from tablexxx;

"

 

报  FAILED: ParseException line cannot recognize input near ',' ',' 'collect_set' in function specification

 

 

处理方式如下:

 

1 将","修改成  ','

 

hive -e "'

    select xxx, concat_ws(',',collect_set(字段))  字段  from tablexxx;

"

 

2 将上面sql原封不动的帖到 xxx.hql中,后用

hive -f xxx.hql 执行

 

 

### Recognize Function, Library, or Command in Programming and IT Context In the context of programming and information technology (IT), a `recognize` function, library, or command typically refers to functionality designed for pattern recognition, object detection, speech recognition, text recognition, or other forms of identifying specific entities within data sets. #### Pattern Recognition Libraries For applications involving machine learning and artificial intelligence, libraries such as TensorFlow[^1], PyTorch, OpenCV, and scikit-learn provide robust tools that include functions named `recognize`. These are used primarily for recognizing patterns from images, sounds, texts, etc., through trained models. For instance: ```python import cv2 face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') img = cv2.imread('people.jpg') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray, 1.1, 4) for (x, y, w, h) in faces: cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2) cv2.imshow('Detected Faces', img) cv2.waitKey() ``` This example uses OpenCV's pre-trained Haar cascade model to recognize human faces in an image file. #### Speech Recognition Commands Python has several packages dedicated specifically to speech-to-text conversion which may contain methods called `recognize`. One popular choice is the `speech_recognition` package by Anthony Zhang: ```python import speech_recognition as sr r = sr.Recognizer() with sr.Microphone() as source: audio_data = r.record(source, duration=5) text = r.recognize_google(audio_data) print(text) ``` Here, Google Web Speech API serves as backend service performing actual voice recognition tasks. #### Text Recognition Tools When dealing with natural language processing (NLP), spaCy offers powerful linguistic features including tokenization, part-of-speech tagging, dependency parsing, entity extraction—all contributing towards better understanding unstructured textual content. Although it does not explicitly define any method labeled 'recognize', its capabilities align closely with what one would expect under this term. ```python import spacy nlp = spacy.load("en_core_web_sm") doc = nlp(u"The quick brown fox jumps over the lazy dog.") for ent in doc.ents: print(ent.text, ent.label_) ``` The above snippet demonstrates how SpaCy recognizes named entities present inside sentences automatically during preprocessing phase without explicit invocation of separate recognizer components.
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