如果在使用AWS Glue时遇到了连接和存储量大导致内存问题，可以尝试通过提高worker的数量来解决。此外，可以使用分区和分桶等优化技巧来减少数据的移动和复制。以下是示例代码：

# 增加worker数量
import sys
from awsglue.utils import getResolvedOptions
from awsglue.job import Job
from pyspark.context import SparkContext
from pyspark.sql.functions import *
from pyspark.sql.types import *
from awsglue.context import GlueContext

args = getResolvedOptions(sys.argv, ['JOB_NAME'])
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)
# 提高worker数量
sc.setCheckpointDir("s3:///checkpoints/")
spark.conf.set("spark.sql.shuffle.partitions", "10") 
spark.conf.set("spark.default.parallelism", "10")
# 加载数据，并进行优化
some_df = glueContext.create_dynamic_frame.from_options(
    connection_type="s3",
    connection_options={"paths": ["s3:///some_data.csv"]},
    format="csv",
    format_options={"withHeader": True},
).toDF().repartition("column1").sortWithinPartitions("column2")
# 进行连接操作
joined_df = some_df.join(another_df, on="column1", how="inner")

# 使用分区和分桶
# 定义schema
schema = StructType([
    StructField("column1", StringType(), True),
    StructField("column2", IntegerType(), True),
    StructField("column3", StringType(), True)])
# 读取数据
df = spark.read \
         .option("header", "true") \
         .option("delimiter", ",") \
         .option("quote", "\"") \
         .schema(schema) \
         .load("s3:///data.csv")
# 进行分区和分桶
df = df.repartition("column1").sortWithinPartitions("column2") \
       .write \
       .bucketBy(numBuckets=5, "column1") \
       .option("path", "s3:///output/") \
       .saveAsTable("my_table_backend")