...
首先安装rclone成功后，进行配置，我们需要配置源和目标，有两种配置方法即通过命令行配置和配置文件（实际上命令行配置后也会写入到该配置文件，文件路径为~/.config/rclone/rclone.conf），文件内容如下
代码块 language text # AWS 配置 [s3] type = s3 provider = AWS env_auth = false access_key_id = XXXXXXXX # 获取方式自行查阅相关文档 secret_access_key = XXXXXXXX # 获取方式自行查阅相关文档 region = ap-northeast-1 location_constraint = ap-northeast-1 acl = private # DigitalOcean 配置 [spaces] type = s3 provider = DigitalOcean env_auth = false access_key_id = XXXXXXXX # 获取方式自行查阅相关文档 secret_access_key = XXXXXXXX # 获取方式自行查阅相关文档 endpoint = sgp1.digitaloceanspaces.com acl = private
执行以下同步命令，将开始同步指定的AWS s3桶，这里举例`flamegraph.starcoin.org`，需要开一个窗口等待执行完成（这里建议在远程机器或者在本地上面开一个可被detach的命令行，否则当前session会被锁定在这里直到同步执行完成），请确保在执行该命令前，DigitalOcean中Space中对应的桶已经存在s3桶，这里举例`cw-to-feishu-westar`，需要开一个窗口等待执行完成（这里建议在远程机器或者在本地上面开一个可被detach的命令行，否则当前session会被锁定在这里直到同步执行完成），请确保在执行该命令前，DigitalOcean中Space中对应的桶已经存在
代码块 language bash # 同步s3 的 cw-to-feishu-westar 桶中的内容到 DigitalOcean中对应的桶 rclone --no-check-certificate sync s3:cw-to-feishu-westar spaces:cw-to-feishu-westar -vv # 同步完成后执行该命令做检查 rclone check s3:cw-to-feishu-westar spaces:cw-to-feishu-westar -vv
...
首先需要创建一个角色专门为快照进行同步，且赋予它两个IAM策略（上面创建快照文档中均有说明）
角色本身有个信任源，需要修改角色信任关系，让其信任OpenSearch服务
代码块 { "Version": "2012-10-17", "Statement": [{ "Sid": "", "Effect": "Allow", "Principal": { "Service": "es.amazonaws.com" }, "Action": "sts:AssumeRole" }] }允许读写桶的策略，这里的策略资源名称为arn:aws:iam::576184071779:policy/es-snapshot-s3-access，
代码块 { "Version": "2012-10-17", "Statement": [ { "Action": [ "s3:ListBucket" ], "Effect": "Allow", "Resource": [ "arn:aws:s3:::elasticserch-snapshot-backup" ] }, { "Action": [ "s3:GetObject", "s3:PutObject", "s3:DeleteObject" ], "Effect": "Allow", "Resource": [ "arn:aws:s3:::elasticserch-snapshot-backup/*" ] } ] }是将这个角色的读写桶的权限交给ElasticSearch（AWS里面叫OpenSearch），这里我们称为权限传递，可见传递角色为arn:aws:iam::576184071779:role/es-snapshot，传递给ES服务
代码块 { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": "iam:PassRole", "Resource": "arn:aws:iam::576184071779:role/es-snapshot" }, { "Effect": "Allow", "Action": "es:ESHttpPut", "Resource": "arn:aws:es:ap-northeast-1:576184071779:domain/starcoin-es2/*" } ] }
使用 awscurl 发起请求创建快照库（这里一定要用awscurl，否则OpenSearch服务不知道请求者的身份），如果已经配置了 AWS CLI，awscurl 可以使用相同的凭证文件（通常位于
~/.aws/credentials）），有了快照库之后才会有快照。代码块 language bash # 创建s3快照库 awscurl --service es --region ap-northeast-1 -XPUT 'https://search-starcoin-es2-47avtmhexhbg7qtynzebcnnu64.ap-northeast-1.es.amazonaws.com/_snapshot/my-snapshot-repo?pretty' -H 'Content-Type: application/json' -d '{"type": "s3", "settings": {"role_arn": "arn:aws:iam::576184071779:role/es-snapshot", "region": "ap-northeast-1", "bucket": "elasticserch-snapshot-backup"}} { "acknowledge": true } # 创建快照 PUT _snapshot/my-snapshot-repo/snapshot-20240917 { "acknowledge": true }在kibana的devtool中查看快照的创建进度，若状态为 SUCCESS 说明创建成功
代码块 GET _snapshot/my-snapshot-repo/snapshot-20240917 { "snapshots" : [ { "snapshot" : "snapshot-20240917", "uuid" : "TVlHLRoMSXupw60xQgsWcA", "version_id" : 7100299, "version" : "7.10.2", "indices" : [ "halley.0727.transfer_journal", "vega.0727.block_ids", "vega.0727.txn_events", "vega.0727.dag_inspector_block", "vega.0727.pending_txns", "halley.0727.block_ids", ".opendistro-anomaly-detector-jobs", "halley.0727.token_info", "barnard.0727.blocks", ".tasks", "proxima.0727.pending_txns", "barnard.0727.txn_events", "vega.0727.dag_inspector_height_group", "main.0727.market_cap", "barnard.0727.txn_infos", "txn_infos", "barnard.0727.market_cap_bak", "opendistro-sample-http-responses", "halley.0727.txn_events", "main.0727.pending_txns", "vega.0727.txn_infos", "proxima.0727.transfer_journal", "proxima.0727.address_holder", "halley.0727.txn_infos", "barnard.0727.txn_payloads", "vega.0727.transfer_journal", "barnard.0727.918.address_holder", ".opendistro-anomaly-detectors", "barnard.0914.txn_infos", ".opendistro-reports-definitions", ".opendistro_security", "main.0727.txn_payloads", "main.0727.token_info", ".opendistro-job-scheduler-lock", "halley.0727.txn_payloads", "main.0727.txn_infos", ".opendistro-anomaly-results-history-2021.05.07-1", "proxima.0727.token_info", "barnard.0727.market_cap", ".opendistro-reports-instances", "barnard.0727.block_ids", "main.0727.transfer_journal", "halley.0727.transfer", "vega.0727.txn_payloads", "halley.0727.address_holder", "vega.0727.market_cap", "proxima.0727.transfer", "vega.0727.uncle_blocks", "vega.0727.address_holder", ".opendistro-anomaly-checkpoints", "vega.0727.token_info", "halley.0727.blocks", "barnard.0727.txn_infos_0915", "main.0727.transfer", "halley.0727.uncle_blocks", ".kibana_1", "barnard.0727.address_holder", "proxima.0727.txn_infos", "proxima.0727.blocks", "halley.0727.market_cap", "proxima.0727.uncle_blocks", "barnard.0727.transfer_journal", "barnard.0727.token_info", "main.0727.uncle_blocks", "barnard.0727.uncle_blocks", "main.0727.block_ids", "vega.0727.blocks", "proxima.0727.market_cap", "barnard.0401.txn_infos", "halley.0727.pending_txns", ".opendistro-anomaly-detection-state", "vega.0727.transfer", "proxima.0727.txn_payloads", "barnard.0727.pending_txns", "main.0727.txn_events", "test_index", "main.0727.blocks", "barnard.0727.transfer", "proxima.0727.block_ids", "main.0727.address_holder", ".kibana_-1666338091_elastic_1", "vega.0727.dag_inspector_edge", "proxima.0727.txn_events" ], "data_streams" : [ ], "include_global_state" : true, "state" : "SUCCESS", "start_time" : "2024-09-17T05:04:52.562Z", "start_time_in_millis" : 1726549492562, "end_time" : "2024-09-17T07:08:33.370Z", "end_time_in_millis" : 1726556913370, "duration_in_millis" : 7420808, "failures" : [ ], "shards" : { "total" : 381, "failed" : 0, "successful" : 381 } } ] }
2. 在目标集群挂载S3
增加配置，这里在命令行做了两件事情：a. 在es服务上面安装s3-repository插件，b. 将aws s3的访问信息加入到es库中
代码块 # elasticsearch-deployment.yaml apiVersion: apps/v1 kind: Deployment metadata: name: elasticsearch spec: replicas: 1 selector: matchLabels: app: elasticsearch template: metadata: labels: app: elasticsearch spec: ... ################## # 新增部分 lifecycle: postStart: exec: command: ["/bin/bash", "-c", "/usr/share/elasticsearch/bin/elasticsearch-plugin list | grep -q repository-s3 || /usr/share/elasticsearch/bin/elasticsearch-plugin install --batch repository-s3 && \ echo ${S3_CLIENT_ACCESS_KEY} | /usr/share/elasticsearch/bin/elasticsearch-keystore add s3.client.default.access_key --stdin &&\ echo ${S3_CLIENT_SECRET_KEY} | /usr/share/elasticsearch/bin/elasticsearch-keystore add s3.client.default.secret_key --stdin"] ################## ... --- # Elasticsearch Configuration apiVersion: v1 kind: ConfigMap metadata: name: elasticsearch-config data: elasticsearch.yml: | ... ################## # 新增部分 s3.client.default.endpoint: "s3.ap-northeast-1.amazonaws.com" s3.client.default.protocol: https s3.client.default.read_timeout: 50s s3.client.default.max_retries: 3 s3.client.default.use_throttle_retries: true ################## ...启动后在目标集群的kibana devtool中创建挂载的快照库
代码块 PUT _snapshot/s3_backup_repository { "type": "s3", "settings": { "region": "ap-northeast-1", "bucket": "elasticserch-snapshot-backup", "compress": true, "server_side_encryption": true, "storage_class": "standard" } } # 若成功，表明s3的快照库关联成功 { "acknowledge": true } # 若失败，则需要检查上一步中的S3_CLIENT_ACCESS_KEY和S3_CLIENT_SECRET_KEY是否成功添加 { "error" : { "root_cause" : [ { "type" : "repository_exception", "reason" : "[s3_backup_repository] Could not determine repository generation from root blobs" } ], "type" : "repository_exception", "reason" : "[s3_backup_repository] Could not determine repository generation from root blobs", "caused_by" : { "type" : "i_o_exception", "reason" : "Exception when listing blobs by prefix [index-]", "caused_by" : { "type" : "sdk_client_exception", "reason" : "The requested metadata is not found at http://169.254.169.254/latest/meta-data/iam/security-credentials/ " } } }, "status" : 500 }检查一下快照库中的快照是否存在，如果存在说明挂载的快照可用，此时就可以进行数据同步了
代码块 language json GET _snapshot/s3_backup_repository/_all { "snapshots" : [ { "snapshot" : "snapshot-20240917", "uuid" : "TVlHLRoMSXupw60xQgsWcA", "repository" : "s3_backup_repository", "version_id" : 7100299, "version" : "7.10.2", "indices" : [ "halley.0727.transfer_journal", "vega.0727.block_ids", "vega.0727.txn_events", "vega.0727.dag_inspector_block", "vega.0727.pending_txns", "halley.0727.block_ids", ".opendistro-anomaly-detector-jobs", "halley.0727.token_info", "barnard.0727.blocks", ".tasks", "proxima.0727.pending_txns", "barnard.0727.txn_events", "vega.0727.dag_inspector_height_group", "main.0727.market_cap", "barnard.0727.txn_infos", "txn_infos", "barnard.0727.market_cap_bak", "opendistro-sample-http-responses", "halley.0727.txn_events", "main.0727.pending_txns", "vega.0727.txn_infos", "proxima.0727.transfer_journal", "proxima.0727.address_holder", "halley.0727.txn_infos", "barnard.0727.txn_payloads", "vega.0727.transfer_journal", "barnard.0727.918.address_holder", ".opendistro-anomaly-detectors", "barnard.0914.txn_infos", ".opendistro-reports-definitions", ".opendistro_security", "main.0727.txn_payloads", "main.0727.token_info", ".opendistro-job-scheduler-lock", "halley.0727.txn_payloads", "main.0727.txn_infos", ".opendistro-anomaly-results-history-2021.05.07-1", "proxima.0727.token_info", "barnard.0727.market_cap", ".opendistro-reports-instances", "barnard.0727.block_ids", "main.0727.transfer_journal", "halley.0727.transfer", "vega.0727.txn_payloads", "halley.0727.address_holder", "vega.0727.market_cap", "proxima.0727.transfer", "vega.0727.uncle_blocks", "vega.0727.address_holder", ".opendistro-anomaly-checkpoints", "vega.0727.token_info", "halley.0727.blocks", "barnard.0727.txn_infos_0915", "main.0727.transfer", "halley.0727.uncle_blocks", ".kibana_1", "barnard.0727.address_holder", "proxima.0727.txn_infos", "proxima.0727.blocks", "halley.0727.market_cap", "proxima.0727.uncle_blocks", "barnard.0727.transfer_journal", "barnard.0727.token_info", "main.0727.uncle_blocks", "barnard.0727.uncle_blocks", "main.0727.block_ids", "vega.0727.blocks", "proxima.0727.market_cap", "barnard.0401.txn_infos", "halley.0727.pending_txns", ".opendistro-anomaly-detection-state", "vega.0727.transfer", "proxima.0727.txn_payloads", "barnard.0727.pending_txns", "main.0727.txn_events", "test_index", "main.0727.blocks", "barnard.0727.transfer", "proxima.0727.block_ids", "main.0727.address_holder", ".kibana_-1666338091_elastic_1", "vega.0727.dag_inspector_edge", "proxima.0727.txn_events" ], "data_streams" : [ ], "include_global_state" : true, "state" : "SUCCESS", "start_time" : "2024-09-17T05:04:52.562Z", "start_time_in_millis" : 1726549492562, "end_time" : "2024-09-17T07:08:33.370Z", "end_time_in_millis" : 1726556913370, "duration_in_millis" : 7420808, "failures" : [ ], "shards" : { "total" : 381, "failed" : 0, "successful" : 381 }, "feature_states" : [ ] } ], "total" : 1, "remaining" : 0 }
3. 在目标集群上同步挂载快照的数据
执行以下命令进行同步
代码块 language json POST _snapshot/s3_backup_repository/snapshot-20240917/_restore { "indices": "barnard.0727.uncle_blocks", "ignore_unavailable": true, "include_global_state": false } # 成功 { "acknowledge": true }查看同步进度，可以通过检查doc的数量与源集群的索引是否一致来判断是否同步完成
代码块 language json GET /barnard.0727.uncle_blocks/_count { "count" : 1109354, "_shards" : { "total" : 5, "successful" : 5, "skipped" : 0, "failed" : 0 } }
Postgresql数据库
pg_dump 格式的迁移
（推荐使用该格式进行迁移）该部分比较简单，由于数据量较小（1.7G），直接使用 pg_dump 工具将其所有数据直接导出即可，之后可以进行导入。
在进行导出之前，先使用以下脚本先清理掉原来库中的一些数据，避免脏数据影响。注意schemaname 要改成需要的schema 名称以免清理错误。
| 代码块 | ||
|---|---|---|
| ||
DO $$
DECLARE
r RECORD;
BEGIN
-- 禁用所有触发器
SET session_replication_role = 'replica';
-- 开始事务
BEGIN
-- 循环遍历指定schema中的所有表
FOR r IN (SELECT tablename FROM pg_tables WHERE schemaname = 'barnard')
LOOP
-- 执行TRUNCATE
EXECUTE 'TRUNCATE TABLE barnard' || quote_ident(r.tablename) || ' CASCADE';
END LOOP;
-- 提交事务
COMMIT;
-- 重新启用触发器
SET session_replication_role = 'origin';
END $$; |
此处为了影响最小，写了一个分离程序，将导出的部分拆分成了main和barnard两个schema
| 代码块 | ||
|---|---|---|
| ||
# How to run
# python postgresql-dump-extractor.py \
# -s barnard \
# -i 576184071779_dump_20240923.sql\
# -o 576184071779_dump_20240923_barnard.sql
import argparse
import re
def extract_copy_statements(input_file, output_file, schema):
with open(input_file, 'r') as infile, open(output_file, 'w') as outfile:
copy_block = []
in_copy_block = False
copy_pattern = re.compile(rf'^COPY {re.escape(schema)}\.(\w+)')
for line in infile:
if not in_copy_block:
if copy_pattern.match(line):
in_copy_block = True
copy_block = [line]
else:
copy_block.append(line)
if line.strip() == '\.':
outfile.writelines(copy_block)
outfile.write('\n')
in_copy_block = False
copy_block = []
def main():
parser = argparse.ArgumentParser(description='Extract COPY statements from PostgreSQL dump file.')
parser.add_argument('-i', '--input', required=True, help='Input dump file name')
parser.add_argument('-o', '--output', required=True, help='Output file name')
parser.add_argument('-s', '--schema', required=True, help='Schema name')
args = parser.parse_args()
extract_copy_statements(args.input, args.output, args.schema)
print(f"Extraction complete. Output written to {args.output}")
if __name__ == "__main__":
main() |
运行以下命令可以将其数据分离出来，注意每次都要指定 schema 命令，之后可以通过psql命令导入分离出来的sql脚本。
| 代码块 | ||
|---|---|---|
| ||
python postgresql-dump-extractor.py \
-s barnard \
-i 576184071779_dump_20240923.sql\
-o 576184071779_dump_20240923_barnard.sql |
基于AWS snapshot格式的迁移
在使用AWS的RDS创建快照的时候，RDS会将其创建为基于Parquet格式的快照https://parquet.apache.org/ ，（关于RDS为何创建为Parquet格式，可能为了考虑兼容性和跨不同的数据服务）在基于这个格式的快照下，我们可以通过创建python文件来读取parquet格式，Python文件如下：
| 代码块 | ||
|---|---|---|
| ||
import pandas as pd
import psycopg2
from pathlib import Path
import os
import logging
import sys
from datetime import datetime
import traceback
# 设置日志
def setup_logging():
# 创建日志目录
log_dir = 'restore_logs'
if not os.path.exists(log_dir):
os.makedirs(log_dir)
# 生成日志文件名，包含时间戳
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
log_file = f'{log_dir}/restore_{timestamp}.log'
# 配置日志
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler(log_file),
logging.StreamHandler(sys.stdout)
]
)
return logging.getLogger(__name__)
def create_schemas(conn, logger):
schemas = ['barnard', 'main', 'halley', 'proxima', 'starcoin_user']
with conn.cursor() as cur:
for schema in schemas:
try:
cur.execute(f"CREATE SCHEMA IF NOT EXISTS {schema}")
logger.info(f"Schema {schema} created or already exists")
except Exception as e:
logger.error(f"Error creating schema {schema}: {str(e)}")
raise
conn.commit()
def get_table_name(file_path):
try:
parts = str(file_path).split('/')
full_table_name = parts[-3] # 例如 'barnard.address_holder'
return full_table_name
except Exception as e:
raise ValueError(f"Invalid file path structure: {file_path}. Error: {str(e)}")
def get_postgres_type(pandas_type):
type_mapping = {
'int64': 'BIGINT',
'float64': 'DOUBLE PRECISION',
'object': 'TEXT',
'bool': 'BOOLEAN',
'datetime64[ns]': 'TIMESTAMP',
'category': 'TEXT'
}
pg_type = type_mapping.get(str(pandas_type), 'TEXT')
return pg_type
def import_parquet_to_postgres(parquet_dir, db_params):
logger = setup_logging()
logger.info(f"Starting import process from directory: {parquet_dir}")
# 统计信息
stats = {
'total_tables': 0,
'successful_tables': 0,
'failed_tables': 0,
'total_rows': 0
}
try:
conn = psycopg2.connect(**db_params)
logger.info("Successfully connected to database")
create_schemas(conn, logger)
# 获取所有parquet文件
parquet_files = list(Path(parquet_dir).rglob('*.parquet'))
stats['total_tables'] = len(parquet_files)
logger.info(f"Found {stats['total_tables']} tables to import")
for path in parquet_files:
table_name = get_table_name(path)
logger.info(f"\nProcessing table: {table_name}")
try:
# 读取parquet文件
logger.info(f"Reading parquet file: {path}")
df = pd.read_parquet(str(path))
row_count = len(df)
logger.info(f"Found {row_count} rows in {table_name}")
# 获取列信息
columns = df.columns
dtypes = df.dtypes
logger.info(f"Columns found: {', '.join(columns)}")
# 创建表
create_table_sql = f"CREATE TABLE IF NOT EXISTS {table_name} ("
column_defs = []
for col, dtype in zip(columns, dtypes):
pg_type = get_postgres_type(dtype)
column_defs.append(f"\"{col}\" {pg_type}")
logger.debug(f"Column {col}: {dtype} -> {pg_type}")
create_table_sql += ", ".join(column_defs) + ")"
with conn.cursor() as cur:
# 检查表是否已存在
schema, table = table_name.split('.')
cur.execute(f"""
SELECT COUNT(*)
FROM information_schema.tables
WHERE table_schema = %s
AND table_name = %s
""", (schema, table))
table_exists = cur.fetchone()[0] > 0
if table_exists:
logger.warning(f"Table {table_name} already exists. Truncating...")
cur.execute(f"TRUNCATE TABLE {table_name}")
else:
logger.info(f"Creating table {table_name}")
cur.execute(create_table_sql)
# 使用COPY命令批量插入数据
logger.info(f"Starting data import for {table_name}")
from io import StringIO
buffer = StringIO()
df.to_csv(buffer, index=False, header=False, sep='\t', na_rep='\\N')
buffer.seek(0)
cur.copy_from(buffer, table_name, columns=columns, null='\\N')
# 验证导入的行数
cur.execute(f"SELECT COUNT(*) FROM {table_name}")
imported_rows = cur.fetchone()[0]
if imported_rows == row_count:
logger.info(f"Successfully imported {imported_rows} rows into {table_name}")
else:
logger.warning(f"Row count mismatch in {table_name}. Expected: {row_count}, Imported: {imported_rows}")
conn.commit()
stats['successful_tables'] += 1
stats['total_rows'] += row_count
logger.info(f"Successfully imported table {table_name}")
except Exception as e:
stats['failed_tables'] += 1
logger.error(f"Error processing table {table_name}:")
logger.error(traceback.format_exc())
conn.rollback()
# 打印最终统计信息
logger.info("\nImport Summary:")
logger.info(f"Total tables processed: {stats['total_tables']}")
logger.info(f"Successfully imported tables: {stats['successful_tables']}")
logger.info(f"Failed tables: {stats['failed_tables']}")
logger.info(f"Total rows imported: {stats['total_rows']}")
except Exception as e:
logger.error("Fatal error in import process:")
logger.error(traceback.format_exc())
raise
finally:
if 'conn' in locals():
conn.close()
logger.info("Database connection closed")
def check_postgres_connection(db_params, logger):
max_attempts = 3
attempt = 0
while attempt < max_attempts:
try:
conn = psycopg2.connect(**db_params)
conn.close()
logger.info("Successfully connected to PostgreSQL")
return True
except psycopg2.OperationalError:
attempt += 1
logger.warning(f"Connection attempt {attempt} failed. Waiting 5 seconds...")
time.sleep(5)
logger.error("Could not connect to PostgreSQL after multiple attempts")
return False
if __name__ == "__main__":
# 数据库连接参数
db_params = {
'dbname': 'starocin-swapinfo',
'user': os.environ.get('POSTGRES_USER', 'postgres'),
'password': os.environ.get('POSTGRES_PASSWORD', 'your_password'),
'host': 'localhost'
}
# 检查数据库连接性
if not check_postgres_connection(db_params, logger):
logger.error("PostgreSQL is not running or not accessible")
sys.exit(1)
try:
parquet_dir = '/root/starcoin'
import_parquet_to_postgres(parquet_dir, db_params)
except Exception as e:
logging.error("程序执行失败")
logging.error(traceback.format_exc())
sys.exit(1)
|